Method for Determining Coronary Artery Disease Risk

ABSTRACT

Markers and methods useful for assessing coronary artery disease in a subject are provided, along with kits for measuring their expression. Also provided are predictive models, based on the markers, as well as computer systems, and software embodiments of the models for scoring and optionally classifying samples.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation U.S. application Ser. No. 12/816,232, filed Jun. 15, 2010, which claims the benefit of U.S. Provisional Application No. 61/187,203, filed Jun. 15, 2009, and U.S. Provisional Application No. 61/245,190, filed Sep. 23, 2009, the entire disclosures of which are hereby incorporated by reference in their entirety for all purposes.

BACKGROUND

1. Field of the Invention

The invention relates to predictive models for determining the extent of coronary artery disease (CAD) risk based on marker expression measurements, to their methods of use, and to computer systems and software for their implementation.

2. Description of the Related Art

Mortality and morbidity from CAD and myocardial infarction (MI) are a major global health burden. Major determinants of current CAD likelihood are sex, age, and chest-pain type.^(1,2) Other risk factors such as diabetes, smoking, dyslipidemia, and family history have been associated with future cardiovascular event risk.³ In addition, atherosclerosis has a systemic inflammatory component including activation and migration of immune cells into the vessel wall.^(4,5) In fact, since such cells are derived from and have interactions with circulating blood, quantitative measurements of circulating blood cell gene expression reflects the extent of CAD.^(6,7) These observations likely reflect both changes in cell type distributions, which have prognostic value for cardiovascular events⁸ and gene expression changes within a specific cell type or lineage.

The “gold standard” for detecting CAD is invasive coronary angiography; however, this is costly, and can pose risk to the patient. Prior to angiography, non-invasive diagnostic modalities such as myocardial perfusion imaging (MPI) and CT-angiography may be used, however these have complications including radiation exposure, contrast agent sensitivity, and only add moderately to obstructive CAD identification.^(9,10)

Unmet Clinical and Scientific Need

A non-invasive blood test that could reliably identify patients with CAD would have significant clinical utility. As such, a major advancement in the fight against atherosclerosis would be the development of non-invasive diagnostic tests that can aid in the diagnosis and assessment of the extent of CAD in patients. Herein the development and validation of an algorithm using marker expression and clinical factors (e.g., age and gender) for such a purpose is described.

SUMMARY

Disclosed herein is a computer-implemented method for scoring a first sample obtained from a subject, including: obtaining a first dataset associated with the first sample, wherein the first dataset includes quantitative expression data for at least one marker set selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7; wherein term 1 includes marker 1, marker 2, and marker 3, wherein marker 1 includes AF161365, wherein marker 2 includes HNRPF or ACBD5, and wherein marker 3 includes TFCP2 or DDX18; wherein term 2 includes marker 4, marker 5, and marker 6, wherein marker 4 includes AF289562 or CD248, wherein marker 5 includes HNRPF or ACBD5, and wherein marker 6 includes TFCP2 or DDX18; wherein term 3 includes marker 7, marker 8, marker 9, and marker 10 wherein marker 7 includes CD79B or CD19, wherein marker 8 includes SPIB or BLK, wherein marker 9 includes CD3D or LCK, and wherein marker 10 includes TMC8 or CCT2; wherein term 4 includes marker 11, marker 12, marker 13, and marker 14, wherein marker 11 includes S100A12 or MMP9, wherein marker 12 includes CLEC4E or ALOX5AP, wherein marker 13 includes S100A8 or NAMPT, and wherein marker 14 includes RPL28 or SSRP1; wherein term 5 includes marker 15, marker 16, marker 17, marker 18, and marker 19, wherein marker 15 includes S100A12 or MMP9, wherein marker 16 includes CLEC4E or ALOX5AP, wherein marker 17 includes S100A8 or NAMPT, wherein marker 18 includes AQP9 or GLT1D1, and wherein marker 19 includes NCF4 or NCF2; wherein term 6 includes marker 20, marker 21, marker 22, marker 23, marker 24, marker 25, and marker 26, wherein marker 20 includes CASP5 or H3F3B, wherein marker 21 includes IL18RAP or TXN, wherein marker 22 includes TNFAIP6 or PLAUR, wherein marker 23 includes IL8RB or BCL2A1, wherein marker 24 includes TNFRSF10C or PTAFR, wherein marker 25 includes KCNE3 or LAMP2, and wherein marker 26 includes TLR4 or TYROBP; and wherein term 7 includes marker 27, marker 28, marker 29, and marker 30, wherein marker 27 includes SLAMF7 or CX3CR1, wherein marker 28 includes KLRC4 or CD8A, wherein marker 29 includes CD3D or LCK, and wherein marker 30 includes TMC8 or CCT2; and determining, by a computer processor, a first score from the first dataset using an interpretation function, wherein the first score is predictive of CAD in the subject.

In an embodiment, the first dataset includes quantitative expression data for at least two marker sets selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7. In an embodiment, the first dataset includes quantitative expression data for at least three marker sets selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7. In an embodiment, the first dataset includes quantitative expression data for at least four marker sets selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7. In an embodiment, the first dataset includes quantitative expression data for at least five marker sets selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7. In an embodiment, the first dataset includes quantitative expression data for at least six marker sets selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7. In an embodiment, the first dataset includes quantitative expression data for the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7.

In an embodiment, the interpretation function is based on a predictive model. In an embodiment, the predictive model is selected from the group consisting of a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, and a tree-based recursive partitioning model. In an embodiment, the predictive model performance is characterized by an area under the curve (AUC) ranging from 0.68 to 0.70. In an embodiment, the predictive model performance is characterized by an AUC ranging from 0.70 to 0.79. In an embodiment, the predictive model performance is characterized by an AUC ranging from 0.80 to 0.89. In an embodiment, the predictive model performance is characterized by an AUC ranging from 0.90 to 0.99.

In an embodiment, the first dataset further includes a clinical factor. In an embodiment, the clinical factor is selected from the group consisting of: age, gender, chest pain type, neutrophil count, ethnicity, disease duration, diastolic blood pressure, systolic blood pressure, a family history parameter, a medical history parameter, a medical symptom parameter, height, weight, a body-mass index, resting heart rate, and smoker/non-smoker status.

In an embodiment, the obtaining the first dataset associated with the first sample includes obtaining the first sample and processing the first sample to experimentally determine the first dataset. In an embodiment, the obtaining the first dataset associated with the first sample includes receiving the first dataset from a third party that has processed the first sample to experimentally determine the first dataset.

In an embodiment, the method includes classifying the first sample according to the first score. In an embodiment, the classifying is predictive of the presence or absence of CAD in the subject. In an embodiment, the classifying is predictive of the extent of CAD in the subject. In an embodiment, the classifying is predictive of the risk of CAD in the subject. In an embodiment, the method includes rating CAD risk based on the first score.

In an embodiment, the first sample includes peripheral blood cells. In an embodiment, the peripheral blood cells include leukocytes. In an embodiment, the first sample includes RNA extracted from peripheral blood cells.

In an embodiment, the quantitative expression data are derived from hybridization data. In an embodiment, the quantitative expression data are derived from polymerase chain reaction data. In an embodiment, the quantitative expression data are derived from an antibody binding assay. In an embodiment, the first dataset is obtained stored on a storage memory.

In an embodiment, the subject is a human. In an embodiment, the subject has stable chest pain. In an embodiment, the subject has typical angina or atypical angina or an anginal equivalent. In an embodiment, the subject has no previous diagnosis of myocardial infarction (MI). In an embodiment, the subject has not had a revascularization procedure. In an embodiment, the subject does not have diabetes. In an embodiment, the subject does not have an inflammatory condition or an infectious condition. In an embodiment, the subject is not currently taking a steroid, an immunosuppressive agent, or a chemotherapeutic agent.

Also described herein is a computer-implemented method for scoring a first sample obtained from a subject, including: obtaining a first dataset associated with the first sample, wherein the first dataset includes quantitative expression data for at least two markers selected from the group consisting of AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A; and determining, by a computer processor, a first score from the first dataset using an interpretation function, wherein the first score is predictive of CAD in the subject.

In an embodiment, the first dataset includes a clinical factor. In an embodiment, the clinical factor is age and/or gender. In an embodiment, the clinical factor is selected from the group consisting of: age, gender, chest pain type, neutrophil count, ethnicity, disease duration, diastolic blood pressure, systolic blood pressure, a family history parameter, a medical history parameter, a medical symptom parameter, height, weight, a body-mass index, resting heart rate, and smoker/non-smoker status.

In an embodiment, the first dataset includes quantitative expression data for at least three markers. In an embodiment, the first dataset includes quantitative expression data for at least four markers. In an embodiment, the first dataset includes quantitative expression data for at least five markers. In an embodiment, the first dataset includes quantitative expression data for at least six markers.

In an embodiment, the interpretation function is based on a predictive model. In an embodiment, the predictive model is selected from the group consisting of a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, and a tree-based recursive partitioning model. In an embodiment, the predictive model performance is characterized by an area under the curve (AUC) ranging from 0.68 to 0.70. In an embodiment, the predictive model performance is characterized by an AUC ranging from 0.70 to 0.79. In an embodiment, the predictive model performance is characterized by an AUC ranging from 0.80 to 0.89. In an embodiment, the predictive model performance is characterized by an AUC ranging from 0.90 to 0.99.

In an embodiment, the obtaining the first dataset associated with the first sample includes obtaining the first sample and processing the first sample to experimentally determine the first dataset. In an embodiment, the obtaining the first dataset associated with the first sample includes receiving the first dataset from a third party that has processed the first sample to experimentally determine the first dataset.

In an embodiment, the method includes classifying the first sample according to the first score. In an embodiment, the classifying is predictive of the presence or absence of CAD in the subject. In an embodiment, the classifying is predictive of the extent of CAD in the subject. In an embodiment, the classifying is predictive of the risk of CAD in the subject. In an embodiment, the method includes rating CAD risk based on the first score.

In an embodiment, the first sample includes peripheral blood cells. In an embodiment, the peripheral blood cells include leukocytes. In an embodiment, the first sample includes RNA extracted from peripheral blood cells.

In an embodiment, the quantitative expression data are derived from hybridization data. In an embodiment, the quantitative expression data are derived from polymerase chain reaction data. In an embodiment, the quantitative expression data are derived from an antibody binding assay. In an embodiment, the first dataset is obtained stored on a storage memory.

In an embodiment, the subject is a human. In an embodiment, the subject has stable chest pain. In an embodiment, the subject has typical angina or atypical angina or an anginal equivalent. In an embodiment, the subject has no previous diagnosis of myocardial infarction (MI). In an embodiment, the subject has not had a revascularization procedure. In an embodiment, the subject does not have diabetes. In an embodiment, the subject does not have an inflammatory condition or an infectious condition. In an embodiment, the subject is not currently taking a steroid, an immunosuppressive agent, or a chemotherapeutic agent.

Also described herein is a system for predicting CAD in a subject, the system including: a storage memory for storing a dataset associated with a sample obtained from the subject, wherein the first dataset includes quantitative expression data for at least one marker set selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7; wherein term 1 includes marker 1, marker 2, and marker 3, wherein marker 1 includes AF161365, wherein marker 2 includes HNRPF or ACBD5, and wherein marker 3 includes TFCP2 or DDX18; wherein term 2 includes marker 4, marker 5, and marker 6, wherein marker 4 includes AF289562 or CD248, wherein marker 5 includes HNRPF or ACBD5, and wherein marker 6 includes TFCP2 or DDX18; wherein term 3 includes marker 7, marker 8, marker 9, and marker 10 wherein marker 7 includes CD79B or CD19, wherein marker 8 includes SPIB or BLK, wherein marker 9 includes CD3D or LCK, and wherein marker 10 includes TMC8 or CCT2; wherein term 4 includes marker 11, marker 12, marker 13, and marker 14, wherein marker 11 includes S100A12 or MMP9, wherein marker 12 includes CLEC4E or ALOX5AP, wherein marker 13 includes S100A8 or NAMPT, and wherein marker 14 includes RPL28 or SSRP1; wherein term 5 includes marker 15, marker 16, marker 17, marker 18, and marker 19, wherein marker 15 includes S100A12 or MMP9, wherein marker 16 includes CLEC4E or ALOX5AP, wherein marker 17 includes S100A8 or NAMPT, wherein marker 18 includes AQP9 or GLT1D1, and wherein marker 19 includes NCF4 or NCF2; wherein term 6 includes marker 20, marker 21, marker 22, marker 23, marker 24, marker 25, and marker 26, wherein marker 20 includes CASP5 or H3F3B, wherein marker 21 includes IL18RAP or TXN, wherein marker 22 includes TNFAIP6 or PLAUR, wherein marker 23 includes IL8RB or BCL2A1, wherein marker 24 includes TNFRSF10C or PTAFR, wherein marker 25 includes KCNE3 or LAMP2, and wherein marker 26 includes TLR4 or TYROBP; and wherein term 7 includes marker 27, marker 28, marker 29, and marker 30, wherein marker 27 includes SLAMF7 or CX3CR1, wherein marker 28 includes KLRC4 or CD8A, wherein marker 29 includes CD3D or LCK, and wherein marker 30 includes TMC8 or CCT2; and a processor communicatively coupled to the storage memory for determining a score with an interpretation function wherein the score is predictive of CAD in the subject.

Also described herein is a computer-readable storage medium storing computer-executable program code, the program code including: program code for storing a dataset associated with a sample obtained from the subject, wherein the first dataset includes quantitative expression data for at least one marker set selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7; wherein term 1 includes marker 1, marker 2, and marker 3, wherein marker 1 includes AF161365, wherein marker 2 includes HNRPF or ACBD5, and wherein marker 3 includes TFCP2 or DDX18; wherein term 2 includes marker 4, marker 5, and marker 6, wherein marker 4 includes AF289562 or CD248, wherein marker 5 includes HNRPF or ACBD5, and wherein marker 6 includes TFCP2 or DDX18; wherein term 3 includes marker 7, marker 8, marker 9, and marker 10 wherein marker 7 includes CD79B or CD19, wherein marker 8 includes SPIB or BLK, wherein marker 9 includes CD3D or LCK, and wherein marker 10 includes TMC8 or CCT2; wherein term 4 includes marker 11, marker 12, marker 13, and marker 14, wherein marker 11 includes S100A12 or MMP9, wherein marker 12 includes CLEC4E or ALOX5AP, wherein marker 13 includes S100A8 or NAMPT, and wherein marker 14 includes RPL28 or SSRP1; wherein term 5 includes marker 15, marker 16, marker 17, marker 18, and marker 19, wherein marker 15 includes S100A12 or MMP9, wherein marker 16 includes CLEC4E or ALOX5AP, wherein marker 17 includes S100A8 or NAMPT, wherein marker 18 includes AQP9 or GLT1D1, and wherein marker 19 includes NCF4 or NCF2; wherein term 6 includes marker 20, marker 21, marker 22, marker 23, marker 24, marker 25, and marker 26, wherein marker 20 includes CASP5 or H3F3B, wherein marker 21 includes IL18RAP or TXN, wherein marker 22 includes TNFAIP6 or PLAUR, wherein marker 23 includes IL8RB or BCL2A1, wherein marker 24 includes TNFRSF10C or PTAFR, wherein marker 25 includes KCNE3 or LAMP2, and wherein marker 26 includes TLR4 or TYROBP; and wherein term 7 includes marker 27, marker 28, marker 29, and marker 30, wherein marker 27 includes SLAMF7 or CX3CR1, wherein marker 28 includes KLRC4 or CD8A, wherein marker 29 includes CD3D or LCK, and wherein marker 30 includes TMC8 or CCT2; and program code for determining a score with an interpretation function wherein the score is predictive of CAD in the subject.

Also described herein is a method for predicting CAD in a subject, including: obtaining a sample from the subject, wherein the sample includes a plurality of analytes; contacting the sample with a reagent; generating a plurality of complexes between the reagent and the plurality of analytes; detecting the plurality of complexes to obtain a dataset associated with the sample, wherein the first dataset includes quantitative expression data for at least one marker set selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7; wherein term 1 includes marker 1, marker 2, and marker 3, wherein marker 1 includes AF161365, wherein marker 2 includes HNRPF or ACBD5, and wherein marker 3 includes TFCP2 or DDX18; wherein term 2 includes marker 4, marker 5, and marker 6, wherein marker 4 includes AF289562 or CD248, wherein marker 5 includes HNRPF or ACBD5, and wherein marker 6 includes TFCP2 or DDX18; wherein term 3 includes marker 7, marker 8, marker 9, and marker 10 wherein marker 7 includes CD79B or CD19, wherein marker 8 includes SPIB or BLK, wherein marker 9 includes CD3D or LCK, and wherein marker 10 includes TMC8 or CCT2; wherein term 4 includes marker 11, marker 12, marker 13, and marker 14, wherein marker 11 includes S100A12 or MMP9, wherein marker 12 includes CLEC4E or ALOX5AP, wherein marker 13 includes S100A8 or NAMPT, and wherein marker 14 includes RPL28 or SSRP1; wherein term 5 includes marker 15, marker 16, marker 17, marker 18, and marker 19, wherein marker 15 includes S100A12 or MMP9, wherein marker 16 includes CLEC4E or ALOX5AP, wherein marker 17 includes S100A8 or NAMPT, wherein marker 18 includes AQP9 or GLT1D1, and wherein marker 19 includes NCF4 or NCF2; wherein term 6 includes marker 20, marker 21, marker 22, marker 23, marker 24, marker 25, and marker 26, wherein marker 20 includes CASP5 or H3F3B, wherein marker 21 includes IL18RAP or TXN, wherein marker 22 includes TNFAIP6 or PLAUR, wherein marker 23 includes IL8RB or BCL2A1, wherein marker 24 includes TNFRSF10C or PTAFR, wherein marker 25 includes KCNE3 or LAMP2, and wherein marker 26 includes TLR4 or TYROBP; and wherein term 7 includes marker 27, marker 28, marker 29, and marker 30, wherein marker 27 includes SLAMF7 or CX3CR1, wherein marker 28 includes KLRC4 or CD8A, wherein marker 29 includes CD3D or LCK, and wherein marker 30 includes TMC8 or CCT2; and determining a score from the dataset using an interpretation function, wherein the score is predictive of CAD in the subject.

Also described herein is a kit for predicting CAD in a subject, including: a set of reagents including a plurality of reagents for determining from a sample obtained from the subject quantitative expression data for at least one marker set selected from the group consisting of the marker sets in term 1, term 2, term 3, term 4, term 5, term 6, and term 7; wherein term 1 includes marker 1, marker 2, and marker 3, wherein marker 1 includes AF161365, wherein marker 2 includes HNRPF or ACBD5, and wherein marker 3 includes TFCP2 or DDX18; wherein term 2 includes marker 4, marker 5, and marker 6, wherein marker 4 includes AF289562 or CD248, wherein marker 5 includes HNRPF or ACBD5, and wherein marker 6 includes TFCP2 or DDX18; wherein term 3 includes marker 7, marker 8, marker 9, and marker 10 wherein marker 7 includes CD79B or CD19, wherein marker 8 includes SPIB or BLK, wherein marker 9 includes CD3D or LCK, and wherein marker 10 includes TMC8 or CCT2; wherein term 4 includes marker 11, marker 12, marker 13, and marker 14, wherein marker 11 includes S100A12 or MMP9, wherein marker 12 includes CLEC4E or ALOX5AP, wherein marker 13 includes S100A8 or NAMPT, and wherein marker 14 includes RPL28 or SSRP1; wherein term 5 includes marker 15, marker 16, marker 17, marker 18, and marker 19, wherein marker 15 includes S100A12 or MMP9, wherein marker 16 includes CLEC4E or ALOX5AP, wherein marker 17 includes S100A8 or NAMPT, wherein marker 18 includes AQP9 or GLT1D1, and wherein marker 19 includes NCF4 or NCF2; wherein term 6 includes marker 20, marker 21, marker 22, marker 23, marker 24, marker 25, and marker 26, wherein marker 20 includes CASP5 or H3F3B, wherein marker 21 includes IL18RAP or TXN, wherein marker 22 includes TNFAIP6 or PLAUR, wherein marker 23 includes IL8RB or BCL2A1, wherein marker 24 includes TNFRSF10C or PTAFR, wherein marker 25 includes KCNE3 or LAMP2, and wherein marker 26 includes TLR4 or TYROBP; and wherein term 7 includes marker 27, marker 28, marker 29, and marker 30, wherein marker 27 includes SLAMF7 or CX3CR1, wherein marker 28 includes KLRC4 or CD8A, wherein marker 29 includes CD3D or LCK, and wherein marker 30 includes TMC8 or CCT2; and instructions for using the plurality of reagents to determine quantitative data from the sample, wherein the instructions include instructions for determining a score from the dataset wherein the score is predictive of CAD in the subject.

In an embodiment, the instructions include instructions for conducting a microarray assay. In an embodiment, the instructions include instructions for conducting a polymerase chain reaction assay.

Also described herein is a system for predicting CAD in a subject, the system including: a storage memory for storing a dataset associated with a sample obtained from the subject, wherein the dataset includes quantitative expression data for at least two markers selected from the group consisting of AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A; and a processor communicatively coupled to the storage memory for determining a score with an interpretation function wherein the score is predictive of CAD in the subject.

Also described herein is a computer-readable storage medium storing computer-executable program code, the program code including: program code for storing a dataset associated with a sample obtained from the subject, wherein the dataset includes quantitative expression data for at least two markers selected from the group consisting of AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A; and program code for determining a score with an interpretation function wherein the score is predictive of CAD in the subject.

Also described herein is a method for predicting CAD in a subject, including: obtaining a sample from the subject, wherein the sample includes a plurality of analytes; contacting the sample with a reagent; generating a plurality of complexes between the reagent and the plurality of analytes; detecting the plurality of complexes to obtain a dataset associated with the sample, wherein the dataset includes quantitative expression data for at least two markers selected from the group consisting of AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A; and determining a score from the dataset using an interpretation function, wherein the score is predictive of CAD in the subject.

Also described herein is a kit for predicting CAD in a subject, including: a set of reagents including a plurality of reagents for determining from a sample obtained from the subject quantitative expression data for at least two markers selected from the group consisting of AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A; and instructions for using the plurality of reagents to determine quantitative data from the sample, wherein the instructions include instructions for determining a score from the dataset, wherein the score is predictive of CAD in the subject.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, and accompanying drawings, where:

FIG. 1—Gene Discovery, Algorithm Development, and Validation Patient and Logic Flow Schematic. Initial gene discovery (CATHGEN repository) included both diabetic and non-diabetic patients. Gene discovery from Personalized Risk Evaluation and Diagnosis in the Coronary Tree (PREDICT) involved non-diabetic patients in a paired microarray analysis, that yielded 655 significant genes in common with those from the CATHGEN arrays. For RT-PCR 113 genes were selected and tested on 640 PREDICT patient samples, from which the final algorithm was derived and locked, followed by validation in the PREDICT validation cohort (N=526).

FIG. 2—RT-PCR Analysis of Diabetics vs Non-diabetic Significant Genes from CATHGEN Microarray analysis. Significance of individual genes selected from the CATHGEN microarray cohort in non-diabetic (ND) and diabetic (D) patients is shown. The sex/age adjusted p values from a CAD logistic regression analysis in each subset are plotted (log scale). Significant p values (<0.05) are indicated in red with gene symbols (upper left quadrant and lower right quadrant), non-significant ones in black (upper right quadrant).

FIG. 3—Venn Diagram of microarray, RT-PCR, and algorithm gene sources. A total of 7718 genes were identified, 2438 and 5935, respectively, from the CATHGEN and PREDICT microarray analyses, with an intersection of 655 genes. For the 113 RT-PCR genes, 52 were from PREDICT, 22 from CATHGEN, and 29 from both; 10 were either normalization genes or from previous studies.⁷ The final algorithm contained 20 informative genes: 10 from both microarray studies, 8 PREDICT alone, and 2 CATHGEN alone.

FIG. 4—Correlation of PCR gene expression to lymphocyte fraction (y-axis) and neutrophil fraction (x-axis) for the 113 PCR genes measured in the PREDICT algorithm development cohort. The range of correlation is up to 0.6 and a total of 42 genes were correlated with neutrophil fraction at >0.2 whereas 39 genes were correlated with lymphocyte count at the same threshold. Genes are identified using the numbering scheme in Table 2.

FIG. 5—Schematic of the Algorithm Structure and Genes. The algorithm consists of overlapping gene expression functions for males and females with a sex-specific linear age function for the former and a non-linear age function for the latter. For the gene expression components, 16/23 genes in 4 terms are gender independent: Term 1—neutrophil activation and apoptosis, Term 3—NK cell activation to T cell ratio, Term 4, B to T cell ratio, and Term 5—AF289562 expression normalized to TFCP2 and HNRPF. In addition, Term 2 consists of 3 sex-independent neutrophil/innate immunity genes (S100A8, S100A12, CLEC4E) normalized to overall neutrophil gene expression (AQP9, NCF4) for females and to RPL28 (lymphocytes) for males. The final male specific term is the normalized expression of TSPAN16. Algorithm score is defined as 1.821−0.755*Term1−0.406*Term3−0.308*Term2*Sex−0.137*Term4−0.548*Term2*(1—Sex)−0.246*Term5−0.481*Term6*Sex+0.851*Sex+0.045*Sex*Age+0.123*(1—Sex)*max(0, Age—55), where Sex is a 0/1 indicator of sex (0=female, 1=male) and age is in years, and is calculated as described (Methods Section below).

FIG. 6—Comparison of Algorithm Performance between Cross-Validation Estimate and Independent Validation. ROC curves of the cross-validation (dashed line) and independent validation (solid line) of the algorithm is shown relative to an AUC of 0.50 (dotted line). The 95% confidence intervals are indicated by the solid areas. The AUC values are: for cross-validation 0.77 (95% CI, 0.73-0.81) and for the independent validation cohort 0.70 (95% CI, 0.65-0.75, p=10⁻¹⁶).

FIG. 7—Allocation of Patients from the PREDICT trial for algorithm development and validation. From a total of 1569 subjects meeting the study inclusion/exclusion criteria 226 were used for gene discovery. The remaining 1343 were divided into independent cohorts for algorithm development (694) and validation (649) as shown; 94% of patients in these cohorts came from the same centers. For algorithm development a total of 640 patient samples were used; 54 were excluded due to incomplete data (Diamond G A, Forrester J S. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med. 1979; 300(24):1350-8.), inadequate blood volume (Stangl V, Witzel V, Baumann G, Stangl K. Current diagnostic concepts to detect coronary artery disease in women. Eur Heart J. 2008; 29(6):707-17.), sex mismatch between experimental and clinical records (Gibbons R J, Abrams J, Chatterjee K, et al. ACC/AHA 2002 guideline update for the management of patients with chronic stable angina—summary article: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee on the Management of Patients With Chronic Stable Angina). J Am Coll Cardiol. 2003; 41(1):159-68.), or statistical outlier assessment (Cook N R, Ridker P M. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009; 150(11):795-802.). For the validation cohort a total of 123 samples were excluded based on: inadequate blood volume or RNA yield (43), significant contamination with genomic DNA (78), or prespecified statistical outlier assessment (2).

FIG. 8—The net benefit curve for a diagnostic as a function of p_(t), a threshold probability that represents the tradeoff between false positives and false negatives. The curve quantifies the net benefit to following the decision rule of score>p_(t)=positive, over a range of possible value for p_(t). The reference lines reflect the net benefit of a) all subjects positive (lower curve) or b) all subjects negative (line at net benefit=0). The net benefit curve for the gene expression algorithm is shown as the top curve, and is greater than either reference line over clinically relevant range for p_(t).

FIG. 9—ROC analysis of Validation Cohort Performance For Algorithm and Clinical Variables. Algorithm performance adds to Clinical Factors by Diamond-Forrester. Comparison of the combination of D-F score and algorithm score (heavy solid line) to D-F score alone (---) in ROC analysis is shown. The AUC=0.50 line (light solid line) is shown for reference. A total of 525 of the 526 validation cohort patients had information available to calculate D-F scores. The AUCs for the two ROC curves are 0.721±0.023 and 0.663±0.025, p=0.003.

FIG. 10—Dependence of Algorithm Score on % Maximum Stenosis in the Validation Cohort. The extent of disease for each patient was quantified by QCA maximum % stenosis and grouped into 5 categories: no measurable disease, 1-24%, 25-49% in ≧1 vessel, 1 vessel ≧50%, and >1 vessel ≧50%. The average algorithm score for each group is illustrated; error bars correspond to 95% confidence intervals.

DETAILED DESCRIPTION Definitions

In general, terms used in the claims and the specification are intended to be construed as having the plain meaning understood by a person of ordinary skill in the art. Certain terms are defined below to provide additional clarity. In case of conflict between the plain meaning and the provided definitions, the provided definitions are to be used.

The term “acute coronary syndrome” encompasses all forms of unstable coronary artery disease.

The term “coronary artery disease” or “CAD” encompasses all forms of atherosclerotic disease affecting the coronary arteries.

The term “Ct” refers to cycle threshold and is defined as the PCR cycle number where the fluorescent value is above a set threshold. Therefore, a low Ct value corresponds to a high level of expression, and a high Ct value corresponds to a low level of expression.

The term “Cp” refers to the crossing point and is defined as the intersection of the best fit of the log-linear portion of a standard's amplification curve in a real time PCR instrument such as, e.g., a LightCycler, and the noise band (set according to background fluorescence measurements).

The term “FDR” means to false discovery rate. FDR can be estimated by analyzing randomly-permuted datasets and tabulating the average number of genes at a given p-value threshold.

The terms “GL” “GM” and “GU” respectively refer to 1st percentile, median, and 99th percentile of Cp for that gene in the Algorithm Development data set.

The terms “marker” or “markers” encompass, without limitation, lipids, lipoproteins, proteins, cytokines, chemokines, growth factors, peptides, nucleic acids, genes, and oligonucleotides, together with their related complexes, metabolites, mutations, variants, polymorphisms, modifications, fragments, subunits, degradation products, elements, and other analytes or sample-derived measures. A marker can also include mutated proteins, mutated nucleic acids, variations in copy numbers, and/or transcript variants, in circumstances in which such mutations, variations in copy number and/or transcript variants are useful for generating a predictive model, or are useful in predictive models developed using related markers (e.g., non-mutated versions of the proteins or nucleic acids, alternative transcripts, etc.).

The terms “highly correlated gene expression” or “highly correlated marker expression” refer to gene or marker expression values that have a sufficient degree of correlation to allow their interchangeable use in a predictive model of coronary artery disease. For example, if gene x having expression value X is used to construct a predictive model, highly correlated gene y having expression value Y can be substituted into the predictive model in a straightforward way readily apparent to those having ordinary skill in the art and the benefit of the instant disclosure. Assuming an approximately linear relationship between the expression values of genes x and y such that Y=a+bX, then X can be substituted into the predictive model with (Y−a)/b. For non-linear correlations, similar mathematical transformations can be used that effectively convert the expression value of gene y into the corresponding expression value for gene x. The terms “highly correlated marker” or “highly correlated substitute marker” refer to markers that can be substituted into and/or added to a predictive model based on, e.g., the above criteria. A highly correlated marker can be used in at least two ways: (1) by substitution of the highly correlated marker(s) for the original marker(s) and generation of a new model for predicting CAD risk; or (2) by substitution of the highly correlated marker(s) for the original marker(s) in the existing model for predicting CAD risk.

The term “mammal” encompasses both humans and non-humans and includes but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines.

The term “metagene” refers to a set of genes whose expression values are combined to generate a single value that can be used as a component in a predictive model. (Brunet, J. P., et al. Proc. Natl. Acad. Sciences 2004; 101(12):4164-9)

The term “myocardial infarction” refers to an ischemic myocardial necrosis. This is usually the result of abrupt reduction in coronary blood flow to a segment of the myocardium, the muscular tissue of the heart. Myocardial infarction can be classified into ST-elevation and non-ST elevation MI (also referred to as unstable angina). Myocardial necrosis results in either classification. Myocardial infarction, of either ST-elevation or non-ST elevation classification, is an unstable form of atherosclerotic cardiovascular disease.

The term “sample” can include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, taken from a subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision, or intervention or other means known in the art.

The term “subject” encompasses a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo, or in vitro, male or female.

The term “obtaining a dataset associated with a sample” encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample, and processing the sample to experimentally determine the data. The phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication or a combination of databases and publications. A dataset can be obtained by one of skill in the art via a variety of known ways including stored on a storage memory.

The term “clinical factor” refers to a measure of a condition of a subject, e.g., disease activity or severity. “Clinical factor” encompasses all markers of a subject's health status, including non-sample markers, and/or other characteristics of a subject, such as, without limitation, age and gender. A clinical factor can be a score, a value, or a set of values that can be obtained from evaluation of a sample (or population of samples) from a subject or a subject under a determined condition. A clinical factor can also be predicted by markers and/or other parameters such as gene expression surrogates.

It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

Methods

Markers and Clinical Factors

The quantity of one or more markers of the invention can be indicated as a value. A value can be one or more numerical values resulting from evaluation of a sample under a condition. The values can be obtained, for example, by experimentally obtaining measures from a sample by an assay performed in a laboratory, or alternatively, obtaining a dataset from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored, e.g., on a storage memory.

In an embodiment, the quantity of one or more markers can be one or more numerical values associated with expression levels of: AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, HNRPF, ACBD5, TFCP2, DDX18, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, CD8A, CD3D, LCK, TMC8, or CCT2; resulting from evaluation of a sample under a condition. This nomenclature is used to refer to human genes in accordance with guidelines provided by the Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC). Further information about each human gene, such as accession number(s) and aliases, can be found by entering the gene name into the search page on the HGNC Search genenames.org website. For example, entering the term “CD3D” into the Simple Search field of the HGNC website on Jun. 1, 2010 returns the approved gene name of CD3D (CD3d molecule, delta (CD3-TCR complex)), the sequence accession IDs of CD3D (X01451; NM_(—)000732), and the previous symbols of CD3D (T3D). Further human gene names are provided in the Examples section below.

In an embodiment, a condition can include one clinical factor or a plurality of clinical factors. In an embodiment, a clinical factor can be included within a dataset. A dataset can include one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more overlapping or distinct clinical factor(s). A clinical factor can be, for example, the condition of a subject in the presence of a disease or in the absence of a disease. Alternatively, or in addition, a clinical factor can be the health status of a subject. Alternatively, or in addition, a clinical factor can be age, gender, chest pain type, neutrophil count, ethnicity, disease duration, diastolic blood pressure, systolic blood pressure, a family history parameter, a medical history parameter, a medical symptom parameter, height, weight, a body-mass index, resting heart rate, and smoker/non-smoker status. Clinical factors can include whether the subject has stable chest pain, whether the subject has typical angina, whether the subject has atypical angina, whether the subject has an anginal equivalent, whether the subject has been previously diagnosed with MI, whether the subject has had a revascularization procedure, whether the subject has diabetes, whether the subject has an inflammatory condition, whether the subject has an infectious condition, whether the subject is taking a steroid, whether the subject is taking an immunosuppressive agent, and/or whether the subject is taking a chemotherapeutic agent. Other examples of clinical factors are listed in the Tables and Figures.

In an embodiment, a marker's associated value can be included in a dataset associated with a sample obtained from a subject. A dataset can include the marker expression value of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more marker(s). For example, a dataset can include the expression values for AF161365, HNRPF, ACBD5; AF161365, HNRPF; or AF161365, ACBD5. Other combinations are described in more detail in the Examples section below.

In an embodiment, one or more markers can be divided into terms. Terms can include one marker, but generally include three or more markers. Terms can be included in a dataset associated with a sample obtained from a subject. The dataset can include one or more terms, two or more terms, three or more terms, four or more terms, five or more terms, six or more terms, seven or more terms, eight or more terms, nine or more terms, or ten or more terms. In an embodiment, a term can include one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more marker(s). In an embodiment, the markers are divided into seven distinct terms: term 1, term 2, term 3, term 4, term 5, term 6, and term 7. In an embodiment, term 1 can include marker 1, marker 2, and marker 3, where marker 1 includes AF161365, where marker 2 includes HNRPF or ACBD5, and where marker 3 includes TFCP2 or DDX18. In an embodiment, term 2 can include marker 4, marker 5, and marker 6, where marker 4 includes AF289562 or CD248, where marker 5 includes HNRPF or ACBD5, and where marker 6 includes TFCP2 or DDX18. In an embodiment, term 3 can include marker 7, marker 8, marker 9, and marker 10 where marker 7 includes CD79B or CD19, where marker 8 includes SPIB or BLK, where marker 9 includes CD3D or LCK, and where marker 10 includes TMC8 or CCT2. In an embodiment, term 4 can include marker 11, marker 12, marker 13, and marker 14, where marker 11 includes S100A12 or MMP9, where marker 12 includes CLEC4E or ALOX5AP, where marker 13 includes S100A8 or NAMPT, and where marker 14 includes RPL28 or SSRP1. In an embodiment, term 5 can include marker 15, marker 16, marker 17, marker 18, and marker 19, where marker 15 includes S100A12 or MMP9, where marker 16 includes CLEC4E or ALOX5AP, where marker 17 includes S100A8 or NAMPT, where marker 18 includes AQP9 or GLT1D1, and where marker 19 includes NCF4 or NCF2. In an embodiment, term 6 can include marker 20, marker 21, marker 22, marker 23, marker 24, marker 25, and marker 26, where marker 20 includes CASP5 or H3F3B, where marker 21 includes IL18RAP or TXN, where marker 22 includes TNFAIP6 or PLAUR, where marker 23 includes IL8RB or BCL2A1, where marker 24 includes TNFRSF10C or PTAFR, where marker 25 includes KCNE3 or LAMP2, and where marker 26 includes TLR4 or TYROBP. In an embodiment, term 7 can include marker 27, marker 28, marker 29, and marker 30, where marker 27 includes SLAMF7 or CX3CR1, where marker 28 includes KLRC4 or CD8A, where marker 29 includes CD3D or LCK, and where marker 30 includes TMC8 or CCT2.

In another embodiment, the invention includes obtaining a sample associated with a subject, where the sample includes one or more markers. The sample can be obtained by the subject or by a third party, e.g., a medical professional. Examples of medical professionals include physicians, emergency medical technicians, nurses, first responders, psychologists, medical physics personnel, nurse practitioners, surgeons, dentists, and any other obvious medical professional as would be known to one skilled in the art. A sample can include peripheral blood cells, isolated leukocytes, or RNA extracted from peripheral blood cells or isolated leukocytes. The sample can be obtained from any bodily fluid, for example, amniotic fluid, aqueous humor, bile, lymph, breast milk, interstitial fluid, blood, blood plasma, cerumen (earwax), Cowper's fluid (pre-ejaculatory fluid), chyle, chyme, female ejaculate, menses, mucus, saliva, urine, vomit, tears, vaginal lubrication, sweat, serum, semen, sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreous humour. In an example, the sample is obtained by a blood draw, where the medical professional draws blood from a subject, such as by a syringe. The bodily fluid can then be tested to determine the value of one or more markers using an assay. The value of the one or more markers can then be evaluated by the same party that performed the assay using the methods of the invention or sent to a third party for evaluation using the methods of the invention.

Interpretation Functions

In an embodiment, an interpretation function can be a function produced by a predictive model. An interpretation function can also be produced by a plurality of predictive models. In an embodiment, an interpretation function can include terms Norm₁, Norm₂, NK_(up), T_(cell), B_(cell), Neut, N_(up), N_(down), SCA₁, AF₂, TSPAN, SEX, and INTERCEPT. In a related embodiment, Norm₁=RPL28, Norm₂=(0.5*HNRPF+0.5*TFCP2), NK_(up)=(0.5*SLAMF7+0.5*KLRC4), T_(cell)=(0.5*CD3D+0.5*TMC8), B_(cell)=(⅔*CD79B+⅓*SPIB), Neut=(0.5*AQP9+0.5*NCF4), N_(up)=(⅓*CASP5+⅓*IL18RAP+⅓*TNFAIP6), N_(down)=(0.25*IL8RB+0.25*TNFRSF10C+0.25*TLR4+0.25*KCNE3), SCA₁=(⅓*S100A12+⅓*CLEC4E+⅓*S100A8), AF₂=AF289562, TSPAN=1 if (AF161365−Norm2>6.27 or AF161365=NoCall), 0 otherwise, SEX=1 for Males, 0 for Females. In a related embodiment, for Males, INTERCEPT=Intercept+SEX+MAGE*Age, with Age in years, and for Females, INTERCEPT=Intercept+OFAGE2*max(0, Age—60), with Age in years. In a related embodiment, coefficients Intercept=1.82120871, SEX=0.851181, OFAGE2=0.123283, MAGE=0.044868, TSPAN=−0.48182, AF2=−0.24592, Bcell=−0.13717, SCA1M=−0.30754, NeutF=−0.54778, Nupdown=−0.75514, and NK=−0.40579. In a related embodiment, a score is determined according to INTERCEPT−Nupdown*(N_(up)−N_(down))−NK*(NK_(up)−T_(cell))−SCA1M*SEX*(SCA₁−Norm₁)−Bcell*(B_(cell)−T_(cell))−NeutF*(1—SEX)*(SCA₁−Neut)−TSPANcoef*SEX*(TSPAN)−AF2*(AF₂−Norm₂). In an embodiment, an interpretation function can include any linear combination of age, gender (i.e., sex), and one or more terms.

In an embodiment, a predictive model can include a partial least squares model, a logistic regression model, a linear regression model, a linear discriminant analysis model, a ridge regression model, and a tree-based recursive partitioning model. In an embodiment, a predictive model can also include Support Vector Machines, quadratic discriminant analysis, or a LASSO regression model. See Elements of Statistical Learning, Springer 2003, Hastie, Tibshirani, Friedman; which is herein incorporated by reference in its entirety for all purposes. Predictive model performance can be characterized by an area under the curve (AUC). In an embodiment, predictive model performance is characterized by an AUC ranging from 0.68 to 0.70. In an embodiment, predictive model performance is characterized by an AUC ranging from 0.70 to 0.79. In an embodiment, predictive model performance is characterized by an AUC ranging from 0.80 to 0.89. In an embodiment, predictive model performance is characterized by an AUC ranging from 0.90 to 0.99.

Assays

Examples of assays for one or more markers include DNA assays, microarrays, polymerase chain reaction (PCR), RT-PCR, Southern blots, Northern blots, antibody-binding assays, enzyme-linked immunosorbent assays (ELISAs), flow cytometry, protein assays, Western blots, nephelometry, turbidimetry, chromatography, mass spectrometry, immunoassays, including, by way of example, but not limitation, RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, or competitive immunoassays, immunoprecipitation, and the assays described in the Examples section below. The information from the assay can be quantitative and sent to a computer system of the invention. The information can also be qualitative, such as observing patterns or fluorescence, which can be translated into a quantitative measure by a user or automatically by a reader or computer system. In an embodiment, the subject can also provide information other than assay information to a computer system, such as race, height, weight, age, gender, eye color, hair color, family medical history and any other information that may be useful to a user, such as a clinical factor described above.

Informative Marker Groups

In addition to the specific, exemplary markers identified in this application by name, accession number, or sequence, included within the scope of the invention are all operable predictive models of CAD and methods for their use to score and optionally classify samples using expression values of variant sequences having at least 90% or at least 95% or at least 97% or greater identity to the exemplified sequences or that encode proteins having sequences with at least 90% or at least 95% or at least 97% or greater identity to those encoded by the exemplified genes or sequences. The percentage of sequence identity may be determined using algorithms well known to those of ordinary skill in the art, including, e.g., BLASTn, and BLASTp, as described in Stephen F. Altschul et al., J. Mol. Biol. 215:403-410 (1990) and available at the National Center for Biotechnology Information website maintained by the National Institutes of Health. As described below, in accordance with an embodiment of the present invention, are all operable predictive models and methods for their use in scoring and optionally classifying samples that use a marker expression measurement that is now known or later discovered to be highly correlated with the expression of an exemplary marker expression value in addition to or in lieu of that exemplary marker expression value. For the purposes of the present invention, such highly correlated genes are contemplated either to be within the literal scope of the claimed inventions or alternatively encompassed as equivalents to the exemplary markers. Identification of markers having expression values that are highly correlated to those of the exemplary markers, and their use as a component of a predictive model is well within the level of ordinary skill in the art. The Examples section below provides numerous examples of methods for identifying highly correlated markers and substituting them for algorithm markers in predictive models of CAD and methods for their use to score and optionally classify samples.

Computer Implementation

In one embodiment, a computer comprises at least one processor coupled to a chipset. Also coupled to the chipset are a memory, a storage device, a keyboard, a graphics adapter, a pointing device, and a network adapter. A display is coupled to the graphics adapter. In one embodiment, the functionality of the chipset is provided by a memory controller hub and an I/O controller hub. In another embodiment, the memory is coupled directly to the processor instead of the chipset.

The storage device is any device capable of holding data, like a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory holds instructions and data used by the processor. The pointing device may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard to input data into the computer system. The graphics adapter displays images and other information on the display. The network adapter couples the computer system to a local or wide area network.

As is known in the art, a computer can have different and/or other components than those described previously. In addition, the computer can lack certain components. Moreover, the storage device can be local and/or remote from the computer (such as embodied within a storage area network (SAN)).

As is known in the art, the computer is adapted to execute computer program modules for providing functionality described herein. As used herein, the term “module” refers to computer program logic utilized to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device, loaded into the memory, and executed by the processor.

The term percent “identity,” in the context of two or more nucleic acid or polypeptide sequences, refer to two or more sequences or subsequences that have a specified percentage of nucleotides or amino acid residues that are the same, when compared and aligned for maximum correspondence, as measured using one of the sequence comparison algorithms described below (e.g., BLASTP and BLASTN or other algorithms available to persons of skill) or by visual inspection. Depending on the application, the percent “identity” can exist over a region of the sequence being compared, e.g., over a functional domain, or, alternatively, exist over the full length of the two sequences to be compared.

For sequence comparison, typically one sequence acts as a reference sequence to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are input into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. The sequence comparison algorithm then calculates the percent sequence identity for the test sequence(s) relative to the reference sequence, based on the designated program parameters.

Optimal alignment of sequences for comparison can be conducted, e.g., by the local homology algorithm of Smith & Waterman, Adv. Appl. Math. 2:482 (1981), by the homology alignment algorithm of Needleman & Wunsch, J. Mol. Biol. 48:443 (1970), by the search for similarity method of Pearson & Lipman, Proc. Nat'l. Acad. Sci. USA 85:2444 (1988), by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Dr., Madison, Wis.), or by visual inspection (see generally Ausubel et al., infra).

One example of an algorithm that is suitable for determining percent sequence identity and sequence similarity is the BLAST algorithm, which is described in Altschul et al., J. Mol. Biol. 215:403-410 (1990). Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information.

Embodiments of the entities described herein can include other and/or different modules than the ones described here. In addition, the functionality attributed to the modules can be performed by other or different modules in other embodiments. Moreover, this description occasionally omits the term “module” for purposes of clarity and convenience.

EXAMPLES

Below are examples of specific embodiments for carrying out the present invention. The examples are offered for illustrative purposes only, and are not intended to limit the scope of the present invention in any way. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperatures, etc.), but some experimental error and deviation should, of course, be allowed for.

The practice of the present invention will employ, unless otherwise indicated, conventional methods of protein chemistry, biochemistry, recombinant DNA techniques and pharmacology, within the skill of the art. Such techniques are explained fully in the literature. See, e.g., T.E. Creighton, Proteins: Structures and Molecular Properties (W.H. Freeman and Company, 1993); A. L. Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et al., Molecular Cloning: A Laboratory Manual (2nd Edition, 1989); Methods In Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.); Remington's Pharmaceutical Sciences, 18th Edition (Easton, Pa.: Mack Publishing Company, 1990); Carey and Sundberg Advanced Organic Chemistry 3^(rd) Ed. (Plenum Press) Vols A and B(1992).

Materials and Methods

General Study Design

The overall study design is shown in FIG. 1. This study had four distinct, consecutive phases. The PREDICT clinical trial registration information is available on the clinicaltrials.gov website at NCT00500617 on May 28, 2010.

Phase 1—CATHGEN DISCOVERY

Phase 1 was Initial Gene Discovery from the Duke University CATHGEN registry, a retrospective blood repository.¹¹ Briefly, 198 subjects (88 cases, 110 controls) from this repository were enrolled between August 2004 and November, 2005. Clinical inclusion and exclusion criteria were described previously and included both diabetic and non-diabetic patients.⁷ All CATHGEN patients gave written informed consent and the study protocol was approved by the Duke University IRB. Microarrays were performed to identify CAD sensitive genes, and a subset of genes was selected for RT-PCR replication. Given the phase I findings, only non-diabetic subjects were included subsequently.

Phase II—PREDICT DISCOVERY.

Phase 2 was a prospective gene discovery phase with subjects from the PREDICT study, where 198 patients (99 case: control pairs, matched for age and sex) underwent microarray analysis to identify differentially expressed genes.

Phase III—PREDICT DEVELOPMENT.

Phase 3 was prospective algorithm development with 640 patients (210 cases, 430 controls) to determine the inter-relationships between clinical factors, blood cell counts, gene expression, and CAD.

Phase IV—PREDICT VALIDATION.

After Phase III was completed the locked algorithm was prospectively validated in an independent cohort of 526 patients (192 cases, 334 controls).

Subjects from PREDICT were eligible if they had a history of chest pain, suspected anginal-equivalent symptoms, or a high risk of CAD with no known prior MI, revascularization, or CAD. Detailed inclusion/exclusion criteria have been described.¹² Diabetic status was defined by clinical identification, blood glucose (non-fasting ≧200 or fasting ≧126), rorhemoglobin A1c, (≧6.5), or diabetic medication prescription. Complete blood counts with differentials were obtained for all patients. PREDICT patients gave written informed consent, and the study protocol was approved by the Western Institutional Review Board.

Blood Collection, RNA Purification, and RT-PCR

Whole blood samples were collected in PAXgene® tubes prior to coronary angiography, according to the manufacturer's instructions, and then frozen at −20° C. For the CATHGEN samples RNA was purified as described (PreAnalytix, Franklin Lakes, N.J.), followed by quantitative analysis (Ribogreen, Molecular Probes, Eugene, Oreg.). For the PREDICT samples an automated method using the Agencourt RNAdvance system was employed. Microarray samples were labeled and hybridized to 41K Human Whole Genome Arrays (Agilent, PN #G4112A) using the manufacturer's protocol. For PREDICT microarrays all matched pairs were labeled and hybridized together to minimize microarray batch effects. Microarray data sets have been deposited in GEO (GSE 20686).

Amplicon design, cDNA synthesis, and RT-PCR were performed as previously described.^(7,12) All PCR reactions were run in triplicate and median values used for analysis. The primers and probes are shown in the Informal Sequence Listing below. The primers and probe for marker CD3D were obtained commercially from Applied Biosystems, Inc. (Assay ID: Hs00174158_ml; Part No. 4331182).

Fractionation of Whole Blood Cells for Cell-Type Specific Gene Expression Measurements

Cell fractionation was performed on fresh blood collected in EDTA tubes. 120 ml blood pooled from 4 different donors was 1:1 diluted with 1×PBS. 15% of the blood was used for granulocyte isolation by density centrifugation and 85% of the blood was used for PBMC isolation prior to T cells, B cells, NK cells, and monocytes fractionation.

Peripheral Blood Mononuclear Cell (PBMC) Isolation

PBMC was isolated by density centrifugation. 20 ml diluted blood was layered on 20 ml Histopaque 1077 (Sigma Cat No. 10771) in 50 ml conical tubes and was centrifuged at room temperature for 30 min at 400×g. The PBMC layer was carefully aspirated into new tubes and washed with 1× phosphate-buffered saline (PBS) twice and centrifuged at 200×g for 10 min. The washed PBMC was re-suspended in cold buffer1 (1×PBS, 0.1% BSA and 2mMEDTA) and stored on ice. 5% of the cells were lysed in RLT buffer (Qiagen RNeasy Mini kit, Cat No. 74104) for pre-selection RNA isolation.

Granulocyte Isolation

Granulocytes (neutrophils, eosinophils, basophils) were purified by density centrifugation using two different density mediums. In 15 ml conical tube, 3 ml Hisopaque 1077 was layered on 3 ml Histopaque 1119 (Sigma Cat No. 11191) and 6 ml of the diluted blood was then layered on Histopaque 1077. The tube was centrifuged at room temperature (RT) for 30 min at 700×g. The granulocyte layer was then aspirated into a new tube and washed twice. The pellet was re-suspended in RLT buffer for granulocyte RNA isolation.

Positive Cell Isolation with Magnetic Beads

The subsequent cell types (T cells, B cells, natural killer (NK) cells, monocytes) were positively selected from PBMC used the following reagents and the recommended procedures.

CD8+ T cells—Dynal® CD8 positive isolation kit (Invitrogen Cat. No. 113.33D)

CD3+ T cells—Dynabeads® CD3 (Invitrogen Cat. No. 111.51D)

CD19+ B cells—Dynabeads® CD19 pan B (Invitrogen Cat. No. 111.43D)

CD14+ Monocytes—Dynabeads® CD14 (monocytes/macrophages) (Invitrogen Cat. No. 111.49D)

CD56+ NK cells—Dynabeads® Pan Mouse IgG (Invitrogen Cat. No. 110.41) cross-linked with mouse anti-human CD56 antibodies (BD bioscience Cat No. 556325)

Briefly, PBMC were incubated with antibody-coupled magnetic beads at 4° C. for 20 min and washed 3 times with buffer 1 on the magnet. The selected cells were then re-suspended in RLT buffer for RNA isolation.

RNA Isolation

The RNA samples in RLT buffer were purified using the Qiagen RNeasy Mini kit following the manufacturer's instructions.

Coronary Angiographic Analysis and Case:Control Definition

All patients were clinically referred for angiography and angiograms were performed based on local, institutional protocols. For CATHGEN patients, clinical angiographic interpretation defined cases as ≧75% maximum stenosis in one major vessel or ≧50% in two vessels and controls as <25% stenosis in all major vessels.

For PREDICT patients, core laboratory QCA reads (Cardiovascular Research Foundation New York) were used for case: control classification. Cases had ≧50% stenosis in at least one major coronary vessel and controls <50% stenosis in all major vessels.

Correlation Between Gene Expression and Cell Type Distributions

Correlations with complete blood counts and database gene expression analysis (SymAtlas) were used to identify highly cell-type selective genes. In addition, whole blood cell fractionation by density centrifugation or through positive antibody selection followed by RT-PCR was performed on specific cell fractions.

Statistical Methods

All statistical methods were performed using the R software package. The statistical methods used are described and referenced in greater detail below.

Array Normalization

Agilent processed signal values for array normalization were scaled to a trimmed mean of 100 and then log 2 transformed. Standard array QC metrics (percent present, pairwise correlation, and signal intensity) were used for quality assessment, resulting in 3 of 198 CATHGEN and 12 of 210 PREDICT samples being excluded.

Array Analysis

For the CATHGEN array, logistic regression (unadjusted and sex/age adjusted) was used to assess gene expression association with case: control status. For the PREDICT array, given the paired design, conditional logistic regression was used. False discovery rates were used to account for multiple comparisons. GOEAST was used to determine over-representation of Gene Ontology (GO) terms.¹³

Gene Selection

Genes for RT-PCR were selected based on significance, fold-change, pathway analysis, and literature support. Hierarchical clustering based on gene: gene correlations ensured that RT-PCR genes represented multiple clusters. Normalization genes were selected based on low variance, moderate to high expression, and no significant association with case: control status, sex, age, or cell counts.

PCR Statistical Analysis

Clinical/demographic factors were assessed for CAD association using univariate and multivariate logistic regression. Gene expression association with CAD and other clinical/demographic factors was assessed by robust logistic regression (unadjusted and sex/age adjusted).⁷

Algorithm Development and Validation

Hierarchical clustering was used to group genes using a correlation cutoff. Clusters were reduced to meta-genes¹⁴ and normalization genes based on correlation structure, known biology, and cell count correlation. For meta-gene pairs with high correlation and opposite disease regulation, ratio terms (differences on the log scale) were defined. Meta-genes independently associated with outcome were selected by the LASSO method, with sex by meta-gene interactions allowed during variable selection.¹⁵

The final algorithm was fit using Ridge regression¹⁶, where the outcome variable was case:control status and the predictors the LASSO-selected meta-genes and sex-specific age terms. Sex was a binary predictor, and age a linear predictor with separate slopes for males, females >60, and females <60. Gene expression term penalization was based on cross-validation and prior evidence. Model performance was estimated using leave-one-out cross-validation. Algorithm performance was validated in an independent patient cohort with ROC analysis as primary endpoint.

Algorithm Calculation and Transformation

Data Preprocessing and QC Steps

-   -   1) Compute median of triplicate wells for each algorithm         gene/sample         -   a. If one well has a no call, take the median of the two             remaining wells         -   b. If two or three wells have a no call, the algorithm gene             receives a no call for that sample     -   2) If AF161365 (TSPAN16) receives a no call, impute the value of         38 as the median value for that gene.     -   3) If any algorithm gene other than AF161365 receives a no call,         the sample fails for Missing Gene Cp. None of the 640 samples in         Algorithm Development would fail this metric.     -   4) Compute the median of the algorithm gene SD's, excluding         AF161365. If this value is greater than 0.15, the sample fails         for High Replicate SD.     -   5) For each algorithm gene i, floor the Cp value by replacing         values less than GL_(i) with GL_(i) This value represents the         1^(st) percentile of Cp for that gene in the Algorithm         Development set.     -   6) For each algorithm gene i, ceiling the Cp value by replacing         values greater than GU_(i) with GU_(i). This value represents         the 99^(th) percentile of Cp for that gene in the Algorithm         Development set.     -   7) For each algorithm gene i, compute the absolute value of the         difference between its Cp value and GM_(i), where GM_(i)         represents the median Cp for that gene in the Algorithm         Development set. Sum this value across the algorithm genes         (excluding AF161365). If the sum is greater than 27.17, the         sample fails for Expression Profile Out of Range. 27.17         represents the largest value of this metric within the Algorithm         Development set.

In certain cases, an algorithm score will not be calculated for a subject. Reasons for this include low PAXgene® tube blood volume, lab QC failure, etc. The frequency of occurrence of these failures will be tabulated, though these subjects will not be included in the analysis set. Subjects with missing Diamond Forrester scores will not be included in the analysis set.

Algorithm Calculation

-   -   1) Define Norm₁=RPL28     -   2) Define Norm₂=(0.5*HNRPF+0.5*TFCP2)     -   3) Define NK_(up)=(0.5*SLAMF7+0.5*KLRC4)     -   4) Define T_(cell)=(0.5*CD3D+0.5*TMC8)     -   5) Define B_(cell)=(⅔*CD79B+⅓*SPIB)     -   6) Define Neut=(0.5*AQP9+0.5*NCF4)     -   7) Define N_(up)=(⅓*CASP5+⅓*IL18RAP+⅓*TNFAIP6)     -   8) Define         N_(down)=(0.25*IL8RB+0.25*TNFRSF10C+0.25*TLR4+0.25*KCNE3)     -   9) Define SCA₁=(⅓*S100A12+⅓*CLEC4E+⅓*S100A8)     -   10) Define AF₂=AF289562     -   11) Define TSPAN=1 if (AF161365-Norm2>6.27 or AF161365=NoCall),         0 otherwise     -   12) Define SEX=1 for Males, 0 for Females     -   13) Define Intercept a. For Males, INTERCEPT=2.672+0.0449*Age b.         For Females, INTERCEPT=1.821+0.123*(Age—60), if negative set to         0     -   14) Define         Score=INTERCEPT−0.755*(N_(up)−N_(down))−0.406*(NK_(up)−T_(cell))−0.308*SEX*(SCA₁−Norm₁)−0.137*(B_(cell)−T_(cell))−0.548*(1—SEX)*(SCA₁−Neut)−0.482*SEX*(TSPAN)—0.246*(AF₂−Norm₂)

Score Transformation

The endpoint analyses defined were performed using raw algorithm scores. For clinical reporting purposes, as well as ease of presentation, raw scores may be transformed into a transformed score with a scale designed for ease of clinical use as follows:

Input is Raw Score

If Raw Score<-2.95, set RawScore=−2.95

If Raw Score>1.57, set RawScore=1.57

Raw Score=2.95+RawScore

Final Score=RawScore*40/4.52

Round Final Score up to nearest integer

If Final Score is greater than 40, set to 40

If Final Score is less than 1, set to 1

Value obtained is the Final Transformed Score

Estimation of Score Variability

A total of 41 replicate samples were tested from a large PAXgene® blood pool. The standard deviation of the raw score for these replicates was 0.13. The confidence interval around a given raw score was then the raw score plus or minus 1.96*0.13. The upper and lower bounds of this confidence interval were linearly transformed to the 0 to 40 scale, and then transformed to a confidence interval around the likelihood using the score to likelihood function described above.

Example 1 Demographic Data

Baseline demographic characteristics of the CATHGEN registry and PREDICT study patient cohorts are shown in Table 1. In general, CAD cases were more frequently men, older, had higher SBP, and more dyslipidemia.

Example 2 Phase I: Initial Gene Discovery (CATHGEN)

A total of 2438 genes showed significant CAD association (p<0.05) in a 195 subject case:control analysis (FIG. 1). Clinical and demographic factor analysis of gene expression showed diabetes as the most significant (p=0.0006, Table 3). Based on statistical significance and biological relevance, 88 genes (Table 4) were selected for RT-PCR analysis on these same samples. CAD-gene expression analysis in non-diabetic and diabetic subsets (N=124 and 71, respectively), showed 42 and 12 significant genes, respectively (p<0.05), with no intersection (FIG. 2). Further work was thus limited to non-diabetics.

We observed a strong diabetes-gene expression interaction effect on CAD risk in the CATHGEN cohort, and thus restricted algorithm development to PREDICT non-diabetics. The CATHGEN diabetic subjects encompassed a range of disease severity and a variety of medications, some of which modulate gene expression and affect cardiovascular disease.¹⁷

Example 3 Phase II: Non-Diabetic Gene Discovery (PREDICT)

Microarray CAD gene discovery on 210 PREDICT patient samples used a paired case:control experimental design, to reduce confounding effects of age, sex, and microarray batch processing. CAD analysis on the 99 case:control pairs after QC exclusions yielded 5935 significant genes (p<0.05) with 655 genes in common with the CATHGEN results (FIG. 3, Table 5).

Pathway Analysis of Discovery Genes

Gene Ontology (GO) analysis of these 655 genes identified 189 significant biological process terms (p<0.05, Table 6), largely reflecting inflammation, cellular and stress response, cell death, and apoptosis. The cellular and molecular ontologies showed enrichment of 32 and 49 terms respectively, including mitochondrial function, apoptotic protease activator activity, and antigen binding.

Gene Selection

A total of 113 genes (Table 2) were selected by statistical significance, biological relevance, and prior association with CAD and gene expression measured by RT-PCR in the PREDICT development cohort. Known cell-type specific markers, those correlated with cell counts in PREDICT, and candidate normalization genes, were also represented.

Example 4 Phase III: Prospective Algorithm Development (PREDICT)

The algorithm was derived using the RT-PCR and clinical data from the PREDICT development cohort. The most significant clinical factors for CAD:gene expression association were age, sex, chest pain type, and neutrophil count. Age and sex were independent risk factors for CAD (Table 1) and showed significant gene expression correlation. Chest pain type was also a significant independent risk factor (p=0.005), but was gene expression independent. Neutrophil count was significantly correlated (positively or negatively) to expression of 93 of 113 RT-PCR genes, and was significantly associated with CAD in males (p=0.049), but not females (p=0.77). Gene expression correlations for all genes to neutrophil and lymphocyte fraction were computed (FIG. 4). A correlation cut-off of >0.2 yielded 39 genes as lymphocyte-associated and 42 genes as neutrophil-associated. Neutrophil-associated genes showed both up and down regulation with CAD status, whereas lymphocyte-associated genes were generally down-regulated. There was significant gender-specific regulation of neutrophil correlated genes (males 40/42 genes up-regulated, females, 41/42 down-regulated) whereas lymphocyte gene down-regulation was gender independent.

Hierarchical clustering of the 113 PCR genes resulted in 18 correlated clusters (Table 2), with finer correlation substructure within the lymphocyte and neutrophil associated genes. There were 3 lymphocyte subgroups representing T-cells (clusters 1, 2, 3), B-cells (cluster 3), and NK cells (cluster 12). Three neutrophil subgroups were also identified: previously described neutrophil genes (IL8RB, S100A8, S100A12, TXN, BCL2A1; cluster 13, 16); newly identified up-regulated neutrophil genes (CLEC4E, CASP5, TNFAIP6; cluster 16) and down-regulated neutrophil genes (KCNE3, TLR4, TNFRSF10C; clusters 13, 14).⁷ The 29 genes in clusters 4-11 did not have clear cell-type association.

Algorithm Derivation

Based on the correlation and cell-type analyses, 15 meta-genes and 3 normalization genes were defined as inputs for model variable selection. Selection by the LASSO method, and weight penalization by Ridge regression resulted in the final, locked algorithm, comprising 20 CAD-associated genes and 3 normalization genes in 6 meta-genes (FIG. 5). The algorithm score was defined as the predicted regression model value.

SUMMARY

The PCR algorithm development set was sufficiently powered to investigate the relationship between CAD, clinical factors, and gene expression. The most significant independent clinical risk factors for CAD were age, gender, and chest pain type, the components of the Diamond-Forrester risk model for CAD likelihood,¹ supporting its use as a reference to assess algorithm performance.¹²

The relationships between age, gender, CAD, and gene expression are complex. Increasing age and male gender are well-known risk-factors for CAD which affects gene expression in circulating cells.^(18,19) The majority of genes measured by RT-PCR in this study correlated with lymphocyte or neutrophil fraction (FIG. 4; r>0.2 for 39 and 42 genes respectively). Genes in the neutrophil-associated group include many we previously identified (clusters 6, 13, 14; Table 2).⁷ Lymphocyte group genes include those known to be expressed in T-cells (CD3, TMC8), B-cells (SPIB, CD79B), and NK-cells (SLAMF7, KLRC4) (Clusters 1,3, and 12, respectively). Lymphocyte-associated gene expression decreases with CAD in a gender-independent fashion, consistent with decreased lymphocyte counts being correlated with increased cardiovascular risk.⁸ In contrast, neutrophil-associated genes display significant sex-specific expression differences with CAD: in males 95% of the neutrophil genes were up-regulated whereas 98% were down-regulated in females, consistent with increased granulocyte counts in males being associated with higher CAD risk, with smaller effects in females.²⁰

Biolojiical Significance of Algorithm Terms

The use of correlated meta-genes as building blocks for the algorithm is significantly reflective of gene expression cell-type specificity. The algorithm genes are expressed selectively in multiple types of circulating cells including neutrophils, NK cells, B and T-lymphocytes²¹, supporting roles for both adaptive and innate immune responses in atherosclerosis.⁴

Algorithm term 1 genes (FIG. 5) preferentially expressed in neutrophils, may reflect neutrophil apoptosis, as caspase-5 is increased with CAD, whereas TNFRSF10C, an anti-apoptotic decoy receptor of TRAIL, is decreased.²² Term 2 genes up-regulated with CAD likely reflect both innate immune activation (S100A8 and S100A12),²³ and a cellular necrosis response (CLEC4E).²⁴ S100A8 and S100A12 are up-regulated in chronic inflammatory conditions, perhaps reflecting a more general pathophysiological signal, consistent with increased CAD in disorders such as rheumatoid arthritis.^(25,26)

Term 2 is normalized in a gender specific manner. In males normalization to RPL28, which is strongly expressed in lymphocytes, reflects the neutrophil to lymphocyte ratio, which is prognostic for death or MI in a CAD population.⁸ In females normalization to AQP9 and NCF4, two CAD insensitive neutrophil genes, permits assessment of neutrophil up-regulation of the S100s and CLEC4E.

Term 3 consists of 2 NK cell receptors, SLAMF7 and KLRC4, normalized to T-cell specific genes (TMC8 and CD3D). SLAMF7 may specifically activate NK cell function, while inhibiting B and T cells.²⁷ KLRC4 is also likely involved in NK cell activation.²⁸ NK cells have been associated with atherosclerosis in both mouse models and humans, and reduced lymphocyte counts associated with cardiac events.^(8,29)

Term 4 is a gene expression based measure of the B/T-cell ratio. The role of T cells is complex, whereas B cells have been shown in mouse models to be athero-protective.^(30,31) In this study apparent up-regulation of B-cell specific genes is correlated with CAD, perhaps indicating an immunological response to disease. The last two terms, based on AF289562 (AF2) and TSPAN16 are genes of unknown function.

Example 5 Phase IV: Prospective Algorithm Validation (PREDICT)

The estimated cross-validated algorithm AUC in ROC analysis in the PREDICT development set was 0.77 (95% CI 0.73 to 0.81); prospective validation in the independent PREDICT validation set of 526 patients (192 cases, 334 controls) yielded an AUC of 0.70 (95% CI=0.65 to 0.75) (FIG. 6).

For algorithm development in Phases III and IV, we used a robust approach, which minimized the effect of any single gene, by using meta-genes as building blocks.^(14,32) Penalized stepwise logistic regression (LASSO) selected significant meta-genes from a 640 patient data set which greatly exceeded the number of candidate variables (15 meta-genes), reducing the likelihood of over-fitting. Further, in order to minimize over-weighting of individual terms, meta-gene coefficients were penalized using Ridge regression.

The cross-validated model AUC was 0.77 (95% CI 0.73 to 0.81), suggesting the algorithm score was a significant CAD predictor, and the validation cohort AUC was 0.70, with overlapping confidence intervals (95% CI=0.65 to 0.75). This modest decrease may reflect an over-optimistic cross-validation estimate, as we did not re-select terms during each iteration.

Thus, using a series of microarray and RT-PCR data sets, comprising more than 1,500 patients, we have derived and validated an algorithm, consisting of the expression levels of 23 genes, sex, and age, which assesses the likelihood of obstructive CAD in non-diabetic patients.

Example 6 Summary of Above Examples

This study presents the development and validation of a whole blood derived RT-PCR based gene-expression algorithm for assessment of obstructive CAD likelihood in non-diabetic patients, and includes several key findings. First, gene expression patterns that differentiate diabetic patients with and without CAD were very different from those for study patients without diabetes. In the initial Gene Discovery Cohort, 2438 genes were differentially expressed in cases versus controls. In the second, PREDICT gene discovery cohort in non-diabetic patients, 5935 genes were differentially expressed and 655 overlapped with the initial gene discovery genes. Based on overall correlations and biological significance, 113 of these 655 genes, were selected for RT-PCR analysis in the independent algorithm development cohort (Phase III), which also identified relationships between clinical factors, cell counts, and gene expression. The algorithm, including 23 gene expression levels, age, and sex, was derived from these data and locked. It was then prospectively shown to have significant diagnostic accuracy in Phase IV, the prospective PREDICT validation cohort, with an AUC of 0.70 (95% CI=0.65 to 0.75; p=10⁻¹⁶).

We consider our results robust, due to at least two factors. First, we used a carefully designed, serial, four-phase study comprising >1,500 patients, with initial microarray-based gene discovery confirmed by quantitative RT-PCR measurements in independent patients. Second, we used QCA to define CAD cases and controls, yielding a more accurate gold standard.

Example 7 Removal of One Term from the Algorithm

In the following series of examples (7-47), we examined the sensitivity of the algorithm and the algorithm development process to differences in terms, markers, and statistical methods. Each example follows the same general procedure: 1) identify a plausible alternative model approach (e.g., fewer terms, alternate markers, etc.); 2) rebuild the algorithm based on that alternative approach, including re-weighting the terms and/or markers as appropriate; and 3) assess whether the new model retains significant predictive accuracy.

The ability of the algorithm to determine the likelihood of CAD in the absence of one out of the seven terms was assessed. A single term was removed sequentially from the algorithm while maintaining the other terms and the clinical factors of age and gender. For example, term 1 was removed from the algorithm while terms 2-7 and the clinical factors (age and gender) remained in the algorithm. The markers in terms 1-7 are shown in the table below. Two statistical methods were used for the assessment: logistic regression and ridge regression. For all analyses, the area under the ROC curve (AUC) was the primary accuracy metric used. AUC was computed using cross validation. For example, when term 1 was removed from the algorithm the altered algorithm was as follows:

Algorithm Calculation (Ridge Regression; Removal of Term 1)

-   -   1) Define Norm₁=RPL28     -   2) Define Norm₂=(0.5*HNRPF+0.5*TFCP2)     -   3) Define NK_(up)=(0.5*SLAMF7+0.5*KLRC4)     -   4) Define T_(cell)=(0.5*CD3D+0.5*TMC8)     -   5) Define B_(cell)=(⅔*CD79B+⅓*SPIB)     -   6) Define Neut=(0.5*AQP9+0.5*NCF4)     -   7) Define N_(up)=(⅓*CASP5+⅓*IL18RAP+⅓*TNFAIP6)     -   8) Define         N_(down)=(0.25*IL8RB+0.25*TNFRSF10C+0.25*TLR4+0.25*KCNE3)     -   9) Define SCA₁=(⅓*MMP9+⅓*CLEC4E+⅓*S100A8)     -   10) Define AF₂=AF289562     -   11) Define SEX=1 for Males, 0 for Females     -   12) Define Intercept         -   a. For Males, INTERCEPT=0.70+0.044*Age         -   b. For Females, INTERCEPT=0.38+0.126*(Age—60), if negative             set to 0     -   13) Define         Score=INTERCEPT−0.39*(N_(up)−N_(down))−0.26*(NK_(up)−T_(cell))−0.33*SEX*(SCA₁−Norm₁)−0.06*(B_(cell)−T_(cell))−0.07*(1—SEX)*(SCA₁−Neut)−0.26*(AF₂−Norm₂)

A similar algorithm development procedure was used for the sequential removal of the other terms in this example as well as examples below. Summary statistics for each of the calculations as well as the mean and standard deviation of the results are shown in Table 7. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 8. All six-term sets tested were significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after removal of one term.

Term Markers Term 1 AF161365, HNRPF, TFCP2 Term 2 AF289562, HNRPF, TFCP2 Term 3 CD79B, SPIB, CD3D, TMC8 Term 4 S100A12, CLEC4E, S100A8, RPL28 Term 5 S100A12, CLEC4E, S100A8, AQP9, NCF4 Term 6 CASP5, IL18RAP, TNFAIP6, IL8RB, TNFRSF10C, KCNE3, TLR4 Term 7 SLAMF7, KLRC4, CD3D, TMC8

Example 8 Removal of Two Terms from the Algorithm

The ability of the algorithm to determine the likelihood of CAD in the absence of two out of the seven terms was assessed. Two distinct terms were removed from the algorithm while maintaining the other terms and the clinical factors of age and gender. For example, terms 6-7 were removed from the algorithm while terms 1-5 and the clinical factors remained in the algorithm. All possible five term combinations were assessed. Two statistical methods were used for the assessment: logistic regression and ridge regression. For all analyses, the area under the ROC curve (AUC) was the primary accuracy metric used. AUC was computed using cross validation. Summary statistics for each of the calculations as well as the mean and standard deviation of the results are shown in Table 9. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 8. All five-term sets tested were significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after removal of two terms.

Example 9 Removal of Three Terms from the Algorithm

The ability of the algorithm to determine the likelihood of CAD in the absence of three out of the seven terms was assessed. Three distinct terms were removed from the algorithm while maintaining the other terms and the clinical factors of age and gender. For example, terms 5-7 were removed from the algorithm while terms 1-4 and the clinical factors remained in the algorithm. All possible four term combinations were assessed. Two statistical methods were used for the assessment: logistic regression and ridge regression. For all analyses, the area under the ROC curve (AUC) was the primary accuracy metric used. AUC was computed using cross validation. Summary statistics for each of the calculations as well as the mean and standard deviation of the results are shown in Table 10. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 8. All four-term sets tested were significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after removal of three terms.

Example 10 Removal of Four Terms from the Algorithm

The ability of the algorithm to determine the likelihood of CAD in the absence of four out of the seven terms was assessed. Four distinct terms were removed from the algorithm while maintaining the other terms and the clinical factors of age and gender. For example, terms 4-7 were removed from the algorithm while terms 1-3 and the clinical factors remained in the algorithm. All possible three term combinations were assessed. Two statistical methods were used for the assessment: logistic regression and ridge regression. For all analyses, the area under the ROC curve (AUC) was the primary accuracy metric used. AUC was computed using cross validation. Summary statistics for each of the calculations as well as the mean and standard deviation of the results are shown in Table 11. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 8. All three-term sets tested were significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after removal of four terms.

Example 11 Removal of Five Terms from the Algorithm

The ability of the algorithm to determine the likelihood of CAD in the absence of five out of the seven terms was assessed. Five distinct terms were removed from the algorithm while maintaining the other terms and the clinical factors of age and gender. For example, terms 3-7 were removed from the algorithm while terms 1-2 and the clinical factors remained in the algorithm. All possible two term combinations were assessed. Two statistical methods were used for the assessment: logistic regression and ridge regression. For all analyses, the area under the ROC curve (AUC) was the primary accuracy metric used. AUC was computed using cross validation. Summary statistics for each of the calculations as well as the mean and standard deviation of the results are shown in Table 12. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 8. All two-term sets tested were significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after removal of five terms.

Example 12 Removal of Six Terms from the Algorithm

The ability of the algorithm to determine the likelihood of CAD in the absence of six out of the seven terms was assessed. Six distinct terms were removed from the algorithm while maintaining the other terms and the clinical factors of age and gender. For example, terms 2-7 were removed from the algorithm while term 1 and the clinical factors remained in the algorithm. Two statistical methods were used for the assessment: logistic regression and ridge regression. For all analyses, the area under the ROC curve (AUC) was the primary accuracy metric used. AUC was computed using cross validation. Summary statistics for each of the calculations as well as the mean and standard deviation of the results are shown in Table 13. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 8. All one-term sets tested were significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after removal of six terms.

Example 13 Removal of all Seven Terms from the Algorithm

The ability of the algorithm to determine the likelihood of CAD in the absence of seven out of the seven marker expression terms was assessed. Seven distinct terms were removed from the algorithm while maintaining the clinical factors of age and gender. Two statistical methods were used for the assessment: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric used. AUC was computed using cross validation. Summary statistics for the calculations are shown in Table 14. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 8. The age plus gender plus zero-marker expression term set tested was significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after removal of all seven marker expression terms. This indicates that the algorithm weighting of gender and age is superior to the weighting of clinical factors in the DF model.

Example 14 Replacement of S100A12 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above (See Table 1b and Table 2). For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, MMP9 was substituted for S100A12 in all relevant terms of the algorithm, here MMP9 was substituted for S100A12 in terms 4 and 5. For example, when S100A12 was replaced in the algorithm with MMP9, the altered algorithm was as follows:

Algorithm Calculation (Logistic Regression, Substitution of MMP9 for S100A12

-   -   1) Define Norm₁=RPL28     -   2) Define Norm₂=(0.5*HNRPF+0.5*TFCP2)     -   3) Define NK_(up)=(0.5*SLAMF7+0.5*KLRC4)     -   4) Define T_(cell)=(0.5*CD3D+0.5*TMC8)     -   5) Define B_(cell)=(⅔*CD79B+⅓*SPIB)     -   6) Define Neut=(0.5*AQP9+0.5*NCF4)     -   7) Define N_(up)=(⅓*CASP5+⅓*IL18RAP+⅓*TNFAIP6)     -   8) Define         N_(down)=(0.25*IL8RB+0.25*TNFRSF10C+0.25*TLR4+0.25*KCNE3)     -   9) Define SCA₁=(⅓*MMP9+⅓*CLEC4E+⅓*S100A8)     -   10) Define AF₂=AF289562     -   11) Define TSPAN=1 if (AF161365−Norm2>6.27 or AF161365=NoCall),         0 otherwise     -   12) Define SEX=1 for Males, 0 for Females     -   13) Define Intercept         -   a. For Males, INTERCEPT=5.28+0.047*Age         -   b. For Females, INTERCEPT=4.44+0.120*(Age—60), if negative             set to 0     -   14) Define         Score=INTERCEPT−1.05*(N_(up)−N_(down))−0.56*(NK_(up)−T_(cell))−0.35*SEX*(SCA₁−Norm₁)−0.30*(B_(cell)−T_(cell))−0.89*(1—SEX)*(SCA₁−Neut)−0.87*SEX*(TSPAN)−0.38*(AF₂−Norm₂)

A similar algorithm development procedure was used in examples below. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 15 Replacement of CLEC4E with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, ALOX5AP was substituted for CLEC4E in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 16 Replacement of S100A8 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, NAMPT was substituted for S100A8 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 17 Replacement of CASP5 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, H3F3B was substituted for CASP5 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 18 Replacement of IL18RAP with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, TXN was substituted for IL18RAP in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 19 Replacement of TNFAIP6 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, PLAUR was substituted for TNFAIP6 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 20 Replacement of AQP9 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, GLT1D1 was substituted for AQP9 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 21 Replacement of NCF4 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, NCF2 was substituted for NCF4 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 22 Replacement of CD3D with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, LCK was substituted for CD3D in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 23 Replacement of TMC8 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, CCT2 was substituted for TMC8 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 24 Replacement of CD79B with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, CD19 was substituted for CD79B in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 25 Replacement of SPIB with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, BLK was substituted for SPIB in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 26 Replacement of HNRPF with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, ACBD5 was substituted for HNRPF in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 27 Replacement of TFCP2 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, DDX18 was substituted for TFCP2 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 28 Replacement of RPL28 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, SSRP1 was substituted for RPL28 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 29 Replacement of AF289562 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, CD248 was substituted for AF289562 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 30 Replacement of SLAMF7 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, CX3 CR1 was substituted for SLAMF7 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 31 Replacement of KLRC4 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, CD8A was substituted for KLRC4 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 32 Replacement of IL8RB with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, BCL2A1 was substituted for IL8RB in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 33 Replacement of TNFRSF10C with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, PTAFR was substituted for TNFRSF10C in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 34 Replacement of KCNE3 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, LAMP2 was substituted for KCNE3 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 35 Replacement of TLR4 with a Highly Correlated Substitute Marker

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. The correlation value for this particular replacement is shown in Table 15. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that substituted one gene at a time. In this example, TYROBP was substituted for TLR4 in all relevant terms of the algorithm. Summary statistics for the calculations are shown in Table 15. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) were considered significantly better than the DF model. See Table 16 for DF AUC. The algorithm with the substitute marker remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of the algorithm marker with the highly correlated substitute marker.

Example 36 Random Replacement of Five Algorithm Markers with Five Distinct, Highly Correlated Substitute Markers

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. See Table 15 for the highly correlated substitute markers. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that randomly substituted five highly correlated markers at a time for five distinct algorithm markers. For the random marker substitutions, 100 iterations each were run and the mean and the standard deviation were calculated. Summary statistics for the calculations are shown in Table 16. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) are considered significantly better than the DF model. The algorithm with the substitute markers remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of five algorithm markers with five highly correlated substitute markers.

Example 37 Random Replacement of Ten Algorithm Markers with Ten Distinct, Highly Correlated Substitute Markers

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. See Table 15 for the highly correlated substitute markers. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that randomly substituted ten highly correlated markers at a time for ten distinct algorithm markers. For the random marker substitutions, 100 iterations each were run and the mean and the standard deviation were calculated. Summary statistics for the calculations are shown in Table 16. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) are considered significantly better than the DF model. The algorithm with the substitute markers remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of ten algorithm markers with ten highly correlated substitute markers.

Example 38 Random Replacement of Fifteen Algorithm Markers with Fifteen Distinct, Highly Correlated Substitute Markers

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. See Table 15 for the highly correlated substitute markers. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that randomly substituted fifteen highly correlated markers at a time for fifteen distinct algorithm markers. For the random marker substitutions, 100 iterations each were run and the mean and the standard deviation were calculated. Summary statistics for the calculations are shown in Table 16. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) are considered significantly better than the DF model. The algorithm with the substitute markers remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of fifteen algorithm markers with fifteen highly correlated substitute markers.

Example 39 Random Replacement of Twenty Algorithm Markers with Twenty Distinct, Highly Correlated Substitute Markers

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. See Table 15 for the highly correlated substitute markers. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that randomly substituted twenty highly correlated markers at a time for twenty distinct algorithm markers. For the random marker substitutions, 100 iterations each were run and the mean and the standard deviation were calculated. Summary statistics for the calculations are shown in Table 16. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) are considered significantly better than the DF model. The algorithm with the substitute markers remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of twenty algorithm markers with twenty highly correlated substitute markers.

Example 40 Random Replacement of all Algorithm Markers with Distinct, Highly Correlated Substitute Markers

For each algorithm marker, a highly correlated, non-algorithm substitute marker was identified from the Phase III PCR data set described above. For each marker, a Pearson correlation value between that marker and all other markers was computed and then we picked the substitute marker with maximal correlation to the algorithm marker of interest. This substitute was the marker with the highest correlation to the algorithm marker, subject to the restriction that a substitute marker was not used more than once in the terms of the algorithm. See Table 15 for the highly correlated substitute markers. Two statistical methods were used for the analysis: logistic regression and ridge regression. For the analysis, the area under the ROC curve (AUC) was the primary accuracy metric. AUC was computed using cross validation. Accuracy was computed for models that randomly substituted highly correlated markers at a time for all algorithm markers. For the random marker substitutions, 100 iterations each were run and the mean was calculated. Summary statistics for the calculations are shown in Table 16. AUC's greater than the upper bound of the confidence interval for the AUC of Diamond Forrester (DF) are considered significantly better than the DF model. The algorithm with the substitute markers remained significantly better than the DF model indicating that the algorithm remains predictive of the likelihood of CAD even after replacement of all algorithm markers with highly correlated substitute markers.

Example 41 Removal of Markers from Term 1

Term 1 algorithm and highly correlated substitute markers were sequentially removed from the algorithm to determine whether the algorithm would remain predictive of the likelihood of CAD in their absence. All other terms and their associated markers were removed from the algorithm, thus in this analysis each term was considered on its own. Each term on the model is a delta term, with n_i markers on the left side of the delta term and m_i markers on the right side of the delta term. We examined two marker ‘reduced terms’ where only one of the n_i left-hand side markers and one of the possible m_i right-hand side markers was used in the term. There were thus n_i*m_i possible two marker reduced terms. We also examined ‘reduced terms’ produced by the sequential removal of markers from the full term for both the algorithm markers as well as the substitute markers.

For each of the reduced terms, models were fit including gender, age, and the reduced term, and cross-validated AUC's were estimated. These cross validated AUC's were compared to the AUC's from a model that included gender, age, and the full term. For each reduced term, we tested whether there was still a statistically significant predictive effect of the term, i.e., whether the decrease in AUC was sufficient to render the marker reduced set not beneficial in prediction of CAD. The same process was repeated for all reduced marker sets where correlated replacement markers were used in place of original algorithm markers.

We found that all reduced terms produced in this analysis remained predictive of CAD. See Table 17.

Example 42 Removal of Markers from Term 2

Term 2 algorithm and highly correlated substitute markers were sequentially removed from the algorithm to determine whether the algorithm would remain predictive of the likelihood of CAD in their absence. All other terms and their associated markers were removed from the algorithm, thus in this analysis each term was considered on its own. Each term on the model is a delta term, with n_i markers on the left side of the delta term and m_i markers on the right side of the delta term. We examined two marker ‘reduced terms’ where only one of the n_i left-hand side markers and one of the possible m_i right-hand side markers was used in the term. There were thus n_i*m_i possible two marker reduced terms. We also examined ‘reduced terms’ produced by the sequential removal of markers from the full term for both the algorithm markers as well as the substitute markers.

For each of the reduced terms, models were fit including gender, age, and the reduced term, and cross-validated AUC's were estimated. These cross validated AUC's were compared to the AUC's from a model that included gender, age, and the full term. For each reduced term, we tested whether there was still a statistically significant predictive effect of the term, i.e., whether the decrease in AUC was sufficient to render the marker reduced set not beneficial in prediction of CAD. The same process was repeated for all reduced marker sets where correlated replacement markers were used in place of original algorithm markers.

We found that all reduced terms produced in this analysis remained predictive of CAD. See Table 18.

Example 43 Removal of Markers from Term 3

Term 3 algorithm and highly correlated substitute markers were sequentially removed from the algorithm to determine whether the algorithm would remain predictive of the likelihood of CAD in their absence. All other terms and their associated markers were removed from the algorithm, thus in this analysis each term was considered on its own. Each term on the model is a delta term, with n_i markers on the left side of the delta term and m_i markers on the right side of the delta term. We examined two marker ‘reduced terms’ where only one of the n_i left-hand side markers and one of the possible m_i right-hand side markers was used in the term. There were thus n_i*m_i possible two marker reduced terms. We also examined ‘reduced terms’ produced by the sequential removal of markers from the full term for both the algorithm markers as well as the substitute markers.

For each of the reduced terms, models were fit including gender, age, and the reduced term, and cross-validated AUC's were estimated. These cross validated AUC's were compared to the AUC's from a model that included gender, age, and the full term. For each reduced term, we tested whether there was still a statistically significant predictive effect of the term, i.e., whether the decrease in AUC was sufficient to render the marker reduced set not beneficial in prediction of CAD. The same process was repeated for all reduced marker sets where correlated replacement markers were used in place of original algorithm markers. In addition, for the two marker reduced sets, the same process was repeated again where one correlated replacement marker was used along with one original algorithm marker.

We found that all reduced terms produced in this analysis remained predictive of CAD, except for: LCK/CCT2/CD19/BLK; LCK/CD19/BLK; CCT2/CD19/BLK; LCK/CCT2/CD19; LCK/CD19; CCT2/CD19; CD3D/CD19; LCK/CD19; and CCT2/CD19. See Table 19. TMC8/CD19 was predictive of CAD when AUC using Ridge regression was calculated, but not when AUC using Logistic Regression was calculated. See Table 19.

Example 44 Removal of Markers from Term 4

Term 4 algorithm and highly correlated substitute markers were sequentially removed from the algorithm to determine whether the algorithm would remain predictive of the likelihood of CAD in their absence. All other terms and their associated markers were removed from the algorithm, thus in this analysis each term was considered on its own. Each term on the model is a delta term, with n_i markers on the left side of the delta term and m_i markers on the right side of the delta term. We examined two marker ‘reduced terms’ where only one of the n_i left-hand side markers and one of the possible m_i right-hand side markers was used in the term. There were thus n_i*m_i possible two marker reduced terms. We also examined ‘reduced terms’ produced by the sequential removal of markers from the full term for both the algorithm markers as well as the substitute markers.

For each of the reduced terms, models were fit including gender, age, and the reduced term, and cross-validated AUC's were estimated. These cross validated AUC's were compared to the AUC's from a model that included gender, age, and the full term. For each reduced term, we tested whether there was still a statistically significant predictive effect of the term, i.e., whether the decrease in AUC was sufficient to render the marker reduced set not beneficial in prediction of CAD. The same process was repeated for all reduced marker sets where correlated replacement markers were used in place of original algorithm markers.

We found that all reduced terms produced in this analysis remained predictive of CAD. See Table 20.

Example 45 Removal of Markers from Term 5

Term 5 algorithm and highly correlated substitute markers were sequentially removed from the algorithm to determine whether the algorithm would remain predictive of the likelihood of CAD in their absence. All other terms and their associated markers were removed from the algorithm, thus in this analysis each term was considered on its own. Each term on the model is a delta term, with n_i markers on the left side of the delta term and m_i markers on the right side of the delta term. We examined two marker ‘reduced terms’ where only one of the n_i left-hand side markers and one of the possible m_i right-hand side markers was used in the term. There were thus n_i*m_i possible two marker reduced terms. We also examined ‘reduced terms’ produced by the sequential removal of markers from the full term for both the algorithm markers as well as the substitute markers.

For each of the reduced terms, models were fit including gender, age, and the reduced term, and cross-validated AUC's were estimated. These cross validated AUC's were compared to the AUC's from a model that included gender, age, and the full term. For each reduced term, we tested whether there was still a statistically significant predictive effect of the term, i.e., whether the decrease in AUC was sufficient to render the marker reduced set not beneficial in prediction of CAD. The same process was repeated for all reduced marker sets where correlated replacement markers were used in place of original algorithm markers. In addition, for the two marker reduced sets, the same process was repeated again where one correlated replacement marker was used along with one original algorithm marker.

We found that all reduced terms produced in this analysis remained predictive of CAD, except for: MMP9/ALOX5AP/GLT1D1/NCF2; MMP9/ALOX5AP/NAMPT/NCF2; MMP9/GLT1D1/NCF2; MMP9/ALOX5AP/NCF2; MMP9/NAMPT/NCF2; MMP9/GLT1D1; ALOX5AP/NCF2; MMP9/NCF2; ALOX5AP/AQP9; and ALOX5AP/NCF2. See Table 21. ALOX5AP/NCF4 was predictive of CAD when AUC using Ridge regression was calculated, but not when AUC using Logistic Regression was calculated. See Table 21.

Example 46 Removal of Markers from Term 6

Term 6 algorithm and highly correlated substitute markers were sequentially removed from the algorithm to determine whether the algorithm would remain predictive of the likelihood of CAD in their absence. All other terms and their associated markers were removed from the algorithm, thus in this analysis each term was considered on its own. Each term on the model is a delta term, with n_i markers on the left side of the delta term and m_i markers on the right side of the delta term. We examined two marker ‘reduced terms’ where only one of the n_i left-hand side markers and one of the possible m_i right-hand side markers was used in the term. There were thus n_i*m_i possible two marker reduced terms. We also examined ‘reduced terms’ produced by the sequential removal of markers from the full term for both the algorithm markers as well as the substitute markers.

For each of the reduced terms, models were fit including gender, age, and the reduced term, and cross-validated AUC's were estimated. These cross validated AUC's were compared to the AUC's from a model that included gender, age, and the full term. For each reduced term, we tested whether there was still a statistically significant predictive effect of the term, i.e., whether the decrease in AUC was sufficient to render the marker reduced set not beneficial in prediction of CAD. The same process was repeated for all reduced marker sets where correlated replacement markers were used in place of original algorithm markers. In addition, for the two marker reduced sets, the same process was repeated again where one correlated replacement marker was used along with one original algorithm marker.

We found that all reduced terms produced in this analysis remained predictive of CAD, except for: H3F3B/TXN/BCL2A1/LAMP2/TYROBP; H3F3B/TXN/BCL2A1/LAMP2; H3F3B/TXN/BCL2A1/TYROBP; TXN/PLAUR/BCL2A1/TYROBP; H3F3B/TXN/PLAUR/BCL2A1; H3F3B/BCL2A1/TYROBP; TXN/BCL2A1/TYROBP; H3F3B/TXN/BCL2A1; H3F3B/TXN/TYROBP; TXN/PLAUR/BCL2A1; TXN/PLAUR/BCL2A1; H3F3B/BCL2A1; H3F3B/TYROBP; TXN/BCL2A1; TXN/TYROBP; TXN/IL8RB; and TXN/TNFRSF10C. See Table 22.

Example 47 Removal of Markers from Term 7

Term 7 algorithm and highly correlated substitute markers were sequentially removed from the algorithm to determine whether the algorithm would remain predictive of the likelihood of CAD in their absence. All other terms and their associated markers were removed from the algorithm, thus in this analysis each term was considered on its own. Each term on the model is a delta term, with n_i markers on the left side of the delta term and m_i markers on the right side of the delta term. We examined two marker ‘reduced terms’ where only one of the n_i left-hand side markers and one of the possible m_i right-hand side markers was used in the term. There were thus n_i*m_i possible two marker reduced terms. We also examined ‘reduced terms’ produced by the sequential removal of markers from the full term for both the algorithm markers as well as the substitute markers.

For each of the reduced terms, models were fit including gender, age, and the reduced term, and cross-validated AUC's were estimated. These cross validated AUC's were compared to the AUC's from a model that included gender, age, and the full term. For each reduced term, we tested whether there was still a statistically significant predictive effect of the term, i.e., whether the decrease in AUC was sufficient to render the marker reduced set not beneficial in prediction of CAD. The same process was repeated for all reduced marker sets where correlated replacement markers were used in place of original algorithm markers. In addition, for the two marker reduced sets, the same process was repeated again where one correlated replacement marker was used along with one original algorithm marker.

We found that all reduced terms produced in this analysis remained predictive of CAD, except for: LCK/CCT2/CX3CR1/CD8A; LCK/CX3CR1/CD8A; CCT2/CX3CR1/CD8A; LCK/CCT2/CD8A; LCK/CD8A; CCT2/CD8A; TMC8/CD8A; and CD3D/CD8A. See Table 23.

Example 48 Validation of the Diagnostic Accuracy of the Algorithm for Assessment of CAD in Non-Diabetic Patients

Herein we report initial prospective validation of a gene expression algorithm for the likelihood of obstructive CAD, defined as one or more coronary atherosclerotic lesions causing ≧50% luminal diameter stenosis, in non-diabetic patients with suspected CAD.

Methods

General Study Design and Study Population

Subjects were enrolled in PREDICT, a 39 center (US) prospective study, between July 2007 and April 2009. The study was approved at the institutional review board at all participating centers and all patients gave written informed consent. Subjects referred for diagnostic coronary angiography were eligible if they had a history of chest pain, suspected anginal-equivalent symptoms, or a high risk of CAD, and no known prior myocardial infarction (MI), revascularization, or obstructive CAD. Subjects were ineligible if at catheterization, they had acute MI, high risk unstable angina, severe non-coronary heart disease (congestive heart failure, cardiomyopathy or valve disease), systemic infectious or inflammatory conditions, or were taking immunosuppressive or chemotherapeutic agents.

From 2418 enrolled subjects who met inclusion criteria, 606 diabetic patients were excluded, as this initial algorithm development and validation was focused on non-diabetics. Of the remaining 1812 patients, 237 had angiographic images unsuitable for QCA and 6 had unusable blood samples. For the remaining 1569 subjects, 226 were used in gene discovery (Elashoff M R, Wingrove J A, Beineke P, et al. Development of a Blood-based Gene Expression Algorithm for Assessment of Obstructive Coronary Artery Disease in Non-Diabetic Patients, submitted. Circulation: Cardiovascular Genetics. 2010); the remaining 1343 were divided into independent algorithm development and validation cohorts (FIG. 7) sequentially based on date of enrollment.

Clinical Evaluation and Quantitative Coronary Angiography

Pre-specified clinical data, including demographics, medications, clinical history and presentation, and MPI results were obtained by research study coordinators at study sites using standardized data collection methods and data were verified by independent study monitors.

Coronary angiograms were analyzed by computer-assisted QCA. Specifically, clinically-indicated coronary angiograms performed according to site protocols were digitized, de-identified and analyzed with a validated quantitative protocol at Cardiovascular Research Foundation, New York, N.Y. (Lansky A J, Popma J J. Qualitative and quantitative angiography Philadelphia, Pa.: Saunders; 1998 Text Book of Interventional Cardiology)). Trained technicians, blinded to clinical and gene expression data, visually identified all lesions >10% diameter stenosis (DS) in vessels with diameter >1.5 mm. Using the CMS Medis system, (Medis, version 7.1, Leiden, the Netherlands), technicians traced the vessel lumen across the lesion between the nearest proximal and distal non-diseased locations. The minimal lumen diameter (MLD), reference lumen diameter (RLD=average diameter of normal segments proximal and distal of lesion) and % DS (% DS=(1−MLD/RLD)×100) were then calculated.

The Diamond-Forrester (D-F) risk score, comprised of age, sex, and chest pain type, was prospectively chosen to evaluate the added value of the gene expression score to clinical factors (Diamond G A, Forrester J S. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med. 1979; 300(24):1350-8). D-F classifications of chest pain type (typical angina, atypical angina and non-anginal chest pain) were assigned based on subject interviews (Diamond G A, Forrester J S. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med. 1979; 300(24):1350-8), and D-F scores assigned (Chaitman B R, Bourassa M G, Davis K, et al. Angiographic prevalence of high-risk coronary artery disease in patient subsets (CASS). Circulation. 1981; 64(2):360-7). Subjects without chest pain symptoms were classified as non-anginal chest pain. MPIs were performed as clinically indicated, according to local protocols, and interpreted by local readers with access to clinical data but not gene expression or catheterization data. MPIs were defined as positive if ≧1 reversible or fixed defect consistent with obstructive CAD was reported. Indeterminate or intermediate defects were considered negative.

Obstructive CAD and Disease Group Definitions

Patients with obstructive CAD (N=192) were defined prospectively as subjects with ≧1 atherosclerotic plaque in a major coronary artery (≧1.5 mm lumen diameter) causing ≧50% luminal diameter stenosis by QCA; non-obstructive CAD (N=334) had no lesions >50%.

Blood Samples

Prior to coronary angiography, venous blood samples were collected in PAXgene® RNA-preservation tubes. Samples were treated according to manufacturer's instructions, then frozen at −20° C.

RNA Purification and RT-PCR

Automated RNA purification from whole blood samples using the Agencourt RNAdvance system, cDNA synthesis, and RT-PCR were performed as described (Elashoff M R, Wingrove J A, Beineke P, et al. Development of a Blood-based Gene Expression Algorithm for Assessment of Obstructive Coronary Artery Disease in Non-Diabetic Patients, submitted. Circulation: Cardiovascular Genetics. 2010.). All PCR reactions were run in triplicate and median values used for analysis. Genomic DNA contamination was detected by comparison of expression values for splice-junction spanning and intronic ADORA3 assays normalized to expression values of TFCP2 and HNRPF. The RPS4Y1 assay was run as confirmation of sex for all patients; patients were excluded if there was an apparent mismatch with clinical data. Sample QC metrics and pass-fail criteria were pre-defined and applied prior to evaluation of results as described (Elashoff M R, Wingrove J A, Beineke P, et al. Development of a Blood-based Gene Expression Algorithm for Assessment of Obstructive Coronary Artery Disease in Non-Diabetic Patients, submitted. Circulation: Cardiovascular Genetics. 2010.).

Statistical Methods

Analyses for Table 24 used SAS Version 9.1 (SAS Institute Inc, Cary, N.C., USA). All other analysis was performed using R Version 2.7 (R Foundation for Statistical Computing, Vienna, Austria). Unless otherwise specified, univariate comparisons for continuous variables were done by t-test and categorical variables by Chi-square test. All reported p-values are two-sided.

Gene Expression Algorithm Score

The algorithm was locked prior to the validation study. Raw algorithm scores were computed from median expression values for the 23 algorithm genes, age and sex as described and used in all statistical analyses; scores were linearly transformed to a 0-40 scale for ease of reporting.

ROC Estimation and AUC Comparison

ROC curves were estimated for the a) gene expression algorithm score, b) the D-F risk score, c) a combined model of algorithm score and D-F risk score, d) MPI, and e) a combined model of algorithm score and MPI. Standard methods (Newson R. Confidence intervals for rank statistics: Somers' D and extensions. Stata Journal. 2006; 6:309-334.) were used to estimate the empirical ROC curves and associated AUCs and AUC standard errors. The Z-test was used to test AUCs versus random (AUC=0.50).

Paired AUC comparisons: i) gene expression algorithm score plus D-F risk score vs D-F risk score, and ii) gene expression algorithm score plus MPI vs MPI; were performed by bootstrap. For each comparison, 10,000 bootstrap iterations were run, and the observed AUC difference computed. The median bootstrapped AUC difference was used to estimate the AUC difference, and the p-value estimated using the empirical distribution of bootstrapped AUC differences (i.e. the observed quantile for 0 AUC difference in the empirical distribution).

Logistic Regression

A series of logistic regression models were fit with disease status as the binary dependent variable, and compared using a likelihood ratio test between nested models. Comparisons were: i) gene expression algorithm score plus D-F risk score versus D-F risk score alone; ii) gene expression algorithm score plus MPI versus MPI alone; iii) gene expression algorithm score versus the demographic component of the gene expression algorithm score.

Correlation of Algorithm Score with Maximum Percent Stenosis

The correlation between algorithm score and percent maximum stenosis as continuous variables was assessed by linear regression. Stenosis values were grouped into five increasing categories (no measurable disease, 1-24%, 25-49% in ≧1 vessel, 1 vessel ≧50%, and >1 vessel ≧50%) and ANOVA was used to test for a linear trend in algorithm score across categories.

Reclassification of Disease Status

Gene expression algorithm score and D-F risk scores were defined as low (0% to <20%), intermediate (≧20%, <50%), and high risk (≧50%) obstructive CAD likelihoods. MPI results were classified as negative (no defect/possible fixed or reversible defect) or positive (fixed or reversible defect). For the D-F risk score analysis, a reclassified subject was defined as i) D-F intermediate risk to low or high algorithm score, ii) D-F high risk to algorithm low risk, or iii) D-F low risk to algorithm high. For the MPI analysis, a reclassified subject included i) MPI positive to low risk based on algorithm score, or ii) MPI negative to high risk based on algorithm score. Net reclassification improvement (NRI) of the gene expression algorithm score (and associated p-value) compared to either the D-F risk score or MPI was computed as described in (Pencina M J, D'Agostino R B, Sr., D'Agostino R B, Jr., Vasan R S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008; 27(2):157-72; discussion 207-12.). NRI is a measure of reclassification clinical benefit, and is sensitive to both the fraction and accuracy of reclassification.

NRI Formula

NRI considers as positive reclassifications those patients whose classification moves in the ‘correct’ direction (disease subjects moving to a higher risk classification and non-disease subjects moving to a lower risk classification). Similarly, NRI considers as negative reclassifications those patients whose classification moves in the incorrect direction (disease subjects moving to a lower risk classification and non-disease subjects moving to a higher risk classification). The NRI formula is then the difference between the fraction of positive reclassifications and the fraction of negative reclassifications.

NRI=(pup,events−pdown,events)−(pup,nonevents−pdown,nonevents)

where:

-   -   pup,events=# events moving up/# events     -   pdown,events=# events moving down# events     -   pup,nonevents=# nonevents moving up/# nonevents     -   pdown,nonevents=#nonevents moving down/# nonevents for         significance testing,     -   z=NRI/(v_(e)+v_(ne))^(1/2)     -   where:     -   v_(e)=(pup,events+pdown,events)/# events     -   v_(ne)=(pup,nonevents+pdown,nonevents)/#nonevents     -   (formulas from {Pencina et al., 2008})

Logistic Regression Analyses

D-F Risk Score Model

Model Term Odds Ratio 95% CI p-value Model AIC D-F risk score 1.018 1.012 to 1.023 <.001 652.53

Gene Expression Algorithm Score+D-F Risk Score

Model Term Odds Ratio 95% CI p-value Model AIC D-F risk score 1.012 1.007 to 1.018 <.001 Gene 1.64 1.37 to 1.96 <.001 622.3 expression algorithm score

MPI Model

Model Term Odds Ratio 95% CI p-value Model AIC MPI 1.52 0.88 to 2.67 .14 388.53

Gene Expression Algorithm Score+MPI

Model Term Odds Ratio 95% CI p-value Model AIC MPI 1.04 0.57 to 1.90 .90 Gene 1.85 1.45 to 2.37 <.001 362.15 expression algorithm score

Net Benefit Analysis

Vickers {Vickers et al., 2008} defines the net benefit curve for a diagnostic as a function of p_(t), a threshold probability that represents the tradeoff between false positives and false negatives. The curve quantifies the net benefit to following the decision rule of score>p_(t)=positive, over a range of possible value for p_(t). The reference lines reflect the net benefit of a) all subjects positive (lower curve in FIG. 8) or b) all subjects negative (line at net benefit=0). The net benefit curve for the gene expression algorithm is the upper curve in FIG. 8, and is greater than either reference line over clinically relevant range for p_(t).

Full Clinical Model

Methods

To further assess the added value of the gene expression algorithm a ‘full’ clinical factor model was developed that incorporated the 11 clinical factors that showed univariate significance (p<0.05) between obstructive disease and no obstructive disease patients in the development set. The 11 factors were:

-   -   sex     -   age     -   chest pain type     -   race     -   statin use     -   aspirin use     -   anti-platelet use     -   ACE inhibitor use     -   systolic blood pressure     -   hypertension     -   dyslipidemia

A logistic regression model was then fit using disease status as the dependent variable and these 11 factors as predictor variables. A subject's ‘full clinical model score’ was the subject's predicted value from this model.

Results

Results are reported for the validation set. The AUC of the full clinical model was 0.732, and the AUC for the gene expression algorithm plus the full clinical model was 0.745 (p=0.09). The nested logistic regression comparison of the gene expression algorithm plus the full clinical model versus the full clinical model alone gave a p-value of 0.014.

The NRI of the gene expression algorithm plus the full clinical model versus the full clinical model alone was 10% (p=0.02).

Discussion

The full clinical model evaluated here further supports the concept that the algorithm score adds to known or apparent clinical factors in the PREDICT population. This model suffers from the lack of independent validation, as has been done for the Diamond-Forrester formulation, hence it's role as primary comparator.

Statistical Outlier Assessment

Samples were classified as gene expression outliers based on the following criterion: Σ|g_(i)−m_(i)|>27, where g_(i) is the expression value for the i'th gene, and m_(i) is the median expression value for the i'th gene across the development set.

Results

A total of 1343 non-diabetic patients from the PREDICT trial, enrolled between July 2007 and April 2009, were sequentially allocated to independent development (N=694) and validation (N=649) sets. The limitation to non-diabetic patients was based on the significant differences observed in CAD classifier gene sets dependent on diabetic status (Elashoff M R, Wingrove J A, Beineke P, et al. Development of a Blood-based Gene Expression Algorithm for Assessment of Obstructive Coronary Artery Disease in Non-Diabetic Patients, submitted. Circulation: Cardiovascular Genetics. 2010.). The patient flow, set assignment, and exclusions are shown in FIG. 7. The demographic and clinical characteristics of these sets by disease status, after exclusions, are summarized in Table 24. The clinical characteristics of the development and validation sets were similar. Overall, subjects were 57% male, 37% had obstructive CAD and 26% had no detectable CAD. Significant clinical or demographic variables that were associated with obstructive CAD in both cohorts were increased age, male sex, chest pain type, elevated systolic blood pressure (all p<0.001), hypertension (p=0.001), and white ethnicity (p=0.015).

The gene expression algorithm was developed as described above, with obstructive CAD defined by QCA as ≧50% stenosis in ≧1 major coronary artery. This corresponds approximately to 65-70% stenosis based on clinical angiographic read. The 23 algorithm genes, grouped in the 6 terms, 4 sex-independent and 2 sex-specific, are shown schematically in the figures. The subsequent analyses are for the independent validation set only.

ROC Analysis

The prospectively defined primary endpoint was the area under the ROC curve for algorithm score prediction of disease status. The AUC was 0.70±0.02, (p<0.001) with independently significant performance in male (0.66) and female subsets (0.65) (p<0.001 for each). As a clinical comparator, we used the Diamond-Forrester (D-F) risk score, which was developed to quantify likelihood of current CAD and validated in a large cohort (Diamond G A, Forrester J S. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med. 1979; 300(24):1350-8.; Chaitman B R, Bourassa M G, Davis K, et al. Angiographic prevalence of high-risk coronary artery disease in patient subsets (CASS). Circulation. 1981; 64(2):360-7.). ROC analysis showed a higher AUC for the combination of algorithm score and D-F risk score, compared to D-F risk score alone (AUC 0.72 versus 0.66, p=0.003, FIG. 9).

The most prevalent form of non-invasive imaging in PREDICT was MPI. In the validation set 310 patients had clinically-indicated MPIs performed, of which 72% were positive. Comparative ROC analysis showed an increased AUC for the combined algorithm score and MPI versus MPI alone (AUC 0.70 versus 0.54, p<0.001).

Sensitivity, Specificity

Sensitivity and specificity were determined at an algorithm score threshold of 14.75, corresponding to a disease likelihood of 20%, with 33% of patients having scores below this value. At this threshold, the sensitivity was 85% with a specificity of 43%, corresponding to negative and positive predictive values of 83% and 46%, respectively.

Regression Analysis

A series of nested logistic regression models (see methods) were used to assess the independent contribution of the algorithm score and other predictors. Algorithm score added to the D-F risk score (p<0.001), and to MPI (p<0.001), and the algorithm gene expression terms added (p=0.003) to the algorithm demographic terms (see methods).

Association with Disease Severity

The algorithm score was correlated with maximum percent stenosis (R=0.34, p<0.001), and the average algorithm score increased monotonically with increasing percent maximum stenosis (p<0.001, FIG. 10). The average scores for patients with and without obstructive CAD were 25 and 17, respectively.

Reclassification

Reclassification may be a more clinically relevant measure of a predictor's comparative performance than standard measures such as AUC (Cook N R, Ridker P M. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009; 150(11):795-802.). Tables 25A and 25B show reclassification results for the gene expression algorithm compared to D-F risk score and MPI. In this study the net reclassification improvement for the gene expression algorithm score compared to the D-F risk score was 20% (p<0.001), and to MPI was 21% (p<0.001).

In subjects with intermediate D-F risk scores, 78% (75/96) of patients were reclassified by the gene expression algorithm. Specifically, for the intermediate D-F group, 22% (21/96) were correctly and 8% (7/96) incorrectly reclassified as low risk; 27% (26/96) were correctly and 22% (21/96) incorrectly reclassified as high risk. An additional 38 D-F low risk subjects (15%) were reclassified as high risk (22 correctly, 16 incorrectly), and 28 D-F high risk subjects (16%) reclassified as low risk (22 correctly, 6 incorrectly). Overall, when reclassification errors occurred, they were to a higher risk category, consistent with the gene expression algorithm having a higher NPV than PPV.

Discussion

This study prospectively validates in non-diabetic patients a non-invasive test for obstructive CAD defined by QCA that is based on gene expression in circulating whole blood cells, age and gender. This study extends our previous work on correlation of gene expression changes in blood with CAD (Wingrove J A, Daniels S E, Sehnert A J, et al. Correlation of Peripheral-Blood Gene Expression With the Extent of Coronary Artery Stenosis. Circulation: Cardiovascular Genetics. 2008; 1(1):31-38.) to prospective validation of a classifier for non-diabetic patients with obstructive CAD by ROC analysis (Elashoff M R, Wingrove J A, Beineke P, et al. Development of a Blood-based Gene Expression Algorithm for Assessment of Obstructive Coronary Artery Disease in Non-Diabetic Patients, submitted. Circulation: Cardiovascular Genetics. 2010.). The test yields a numeric score (0-40) with higher scores corresponding to higher likelihood of obstructive CAD and higher maximum percent stenosis.

It has been suggested that reclassification of patient clinical risk or status, as captured by the NRI, may be a more appropriate measure than comparative ROC analysis for evaluating potential biomarkers (Pencina M J, D'Agostino R B, Sr., D'Agostino R B, Jr., Vasan R S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008; 27(2):157-72; discussion 207-12.; Cook N R, Ridker P M. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009; 150(11):795-802.). The gene expression algorithm score improves the accuracy of clinical CAD assessment as shown by an NRI of 20% relative to the D-F score. For the most prevalent non-invasive test, MPI, the NRI was 21%, although these results are likely confounded by the referral bias inherent in this angiographically referred population. Overall, independent of MPI result or D-F risk category, increasing gene expression score leads to monotonically increased risk of obstructive CAD (Table 25A, B).

This gene-expression test could have clinical advantages over current non-invasive CAD diagnostic modalities since it requires only a standard venous blood draw, and no need for radiation, intravenous contrast, or physiologic and pharmacologic stressors. One potential clinical benefit of improving non-invasive assessment of CAD is to reduce invasive diagnostic coronary angiograms in patients without obstructive CAD. In the validation cohort, for example, only 37% of patients undergoing invasive angiography had obstructive CAD and the rate was particularly low in women (26%). A similar overall rate of obstructive CAD on angiography for patients without prior known CAD in a very large registry was recently reported, with little sensitivity to the exact definition of obstructive CAD (Patel M R, Peterson E D, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med. 2010; 362(10):886-95.). The gene-expression test described here identified a low-likelihood (<20%) of obstructive CAD in 33% of patients referred for invasive angiography, although the majority of these patients were also at low risk by clinical factor analysis (Table 25A).

CONCLUSIONS

We describe the prospective multi-center validation of a peripheral blood-based gene expression test to determine the likelihood of obstructive CAD in non-diabetic patients as defined by invasive angiography. This test provides additional information to clinical factors and non-invasive imaging as measured by patient CAD status classification. Clinical use of this test may reduce further testing of patients with suspected CAD.

While the invention has been particularly shown and described with reference to a preferred embodiment and various alternate embodiments, it will be understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the spirit and scope of the invention.

All references, issued patents and patent applications cited within the body of the instant specification are hereby incorporated by reference in their entirety, for all purposes.

REFERENCES

-   1. Diamond G A, Forrester J S. Analysis of probability as an aid in     the clinical diagnosis of coronary-artery disease. N Engl J Med.     1979; 300(24):1350-1358. -   2. Chaitman B R, Bourassa M G, Davis K, Rogers W J, Tyras D H,     Berger R, Kennedy J W, Fisher L, Judkins M P, Mock M B, Killip T.     Angiographic prevalence of high-risk coronary artery disease in     patient subsets (CASS). Circulation. 1981; 64(2):360-367. -   3. Ridker P M, Buring J E, Rifai N, Cook N R. Development and     validation of improved algorithms for the assessment of global     cardiovascular risk in women: the Reynolds Risk Score. Jama. 2007;     297(6):611-619. -   4. Hansson G K, Libby P, Schonbeck U, Yan Z Q. Innate and adaptive     immunity in the pathogenesis of atherosclerosis. Circ Res. 2002;     91(4):281-291. -   5. Libby P, Ridker P M, Maseri A. Inflammation and atherosclerosis.     Circulation. 2002; 105(9):1135-1143. -   6. Sinnaeve P R, Donahue M P, Grass P, Seo D, Vonderscher J, Chibout     S D, Kraus W E, Sketch M, Jr., Nelson C, Ginsburg G S,     Goldschmidt-Clermont P J, Granger C B. Gene expression patterns in     peripheral blood correlate with the extent of coronary artery     disease. PLoS One. 2009; 4(9):e7037. -   7. Wingrove J A, Daniels S E, Sehnert A J, Tingley W, Elashoff M R,     Rosenberg S, Buellesfeld L, Grube E, Newby L K, Ginsburg G S, Kraus     W E. Correlation of Peripheral-Blood Gene Expression With the Extent     of Coronary Artery Stenosis. Circulation: Cardiovascular Genetics.     2008; 1(1):31-38. -   8. Home B D, Anderson J L, John J M, Weaver A, Bair T L, Jensen K R,     Renlund D G, Muhlestein J B. Which white blood cell subtypes predict     increased cardiovascular risk? J Am Coll Cardiol. 2005;     45(10):1638-1643. -   9. Gibbons R J, Abrams J, Chatterjee K, Daley J, Deedwania P C,     Douglas J S, Ferguson T B, Jr., Fihn S D, Fraker T D, Jr., Gardin J     M, O'Rourke R A, Pasternak R C, Williams S V. ACC/AHA 2002 guideline     update for the management of patients with chronic stable     angina—summary article: a report of the American College of     Cardiology/American Heart Association Task Force on practice     guidelines (Committee on the Management of Patients With Chronic     Stable angina). J Am Coll Cardiol. 2003; 41(1):159-168. -   10. Patel M R, Peterson E D, Dai D, Brennan J M, Redberg R F,     Anderson H V, Brindis R G, Douglas P S. Low diagnostic yield of     elective coronary angiography. N Engl J Med. 2010; 362(10):886-895. -   11. Wang L, Hauser E R, Shah S H, Pericak-Vance M A, Haynes C,     Crosslin D, Harris M, Nelson S, Hale A B, Granger C B, Haines J L,     Jones C J, Crossman D, Seo D, Gregory S G, Kraus W E,     Goldschmidt-Clermont P J, Vance J M. Peakwide mapping on chromosome     3q13 identifies the kalirin gene as a novel candidate gene for     coronary artery disease. Am J Hum Genet. 2007; 80(4):650-663. -   12. Rosenberg S, Elashoff M R, Beineke P, Daniels S E, Wingrove J A,     Tingley W G, Sager P T, Sehnert A J, Yau Y, Kraus W, Newby L,     Schwartz R, Voros S, Ellis S, Tahirkheli N, Waksman R, McPherson J,     Lansky A, Schork N, Winn M, Topol E. Multi-Center Validation of the     Diagnostic Accuracy of a Blood-based Gene Expression Test for     Assessment of Obstructive Coronary Artery Disease in Non-Diabetic     Patients. Submitted; 2010. -   13. Zheng Q, Wang X J. GOEAST: a web-based software toolkit for Gene     Ontology enrichment analysis. Nucleic Acids Res. 2008; 36(Web Server     issue):W358-363. -   14. Brunet J P, Tamayo P, Golub T R, Mesirov J P. Metagenes and     molecular pattern discovery using matrix factorization. Proc Natl     Acad Sci USA. 2004; 101(12):4164-4169. -   15. Tibshirani R. Regression shrinkage and selection via the     lasso. J. Royal Statistical Society B. 1996; 58:267-288. -   16. Brown P J. Measurement, Regression, and Calibration. Oxford, U     K: Oxford University -   Press; 1994. -   17. Hamblin M, Chang L, Fan Y, Zhang J, Chen Y E. PPARs and the     cardiovascular system. Antioxid Redox Signal. 2009; 11(6):1415-1452. -   18. Ellegren H, Parsch J. The evolution of sex-biased genes and     sex-biased gene expression. Nat Rev Genet. 2007; 8(9):689-698. -   19. Hong M G, Myers A J, Magnusson P K, Prince J A.     Transcriptome-wide assessment of human brain and lymphocyte     senescence. PLoS One. 2008; 3(8):e3024. -   20. Rana J S, Boekholdt S M, Ridker P M, Jukema J W, Luben R,     Bingham S A, Day N E, Wareham N J, Kastelein J J, Khaw K T.     Differential leucocyte count and the risk of future coronary artery     disease in healthy men and women: the EPIC-Norfolk Prospective     Population Study. J Intern Med. 2007; 262(6):678-689. -   21. Su A I, Wiltshire T, Batalov S, Lapp H, Ching K A, Block D,     Zhang J, Soden R, Hayakawa M, Kreiman G, Cooke M P, Walker J R,     Hogenesch J B. A gene atlas of the mouse and human protein-encoding     transcriptomes. Proc Natl Acad Sci USA. 2004; 101(16):6062-6067. -   22. Hasegawa H, Yamada Y, Harasawa H, Tsuji T, Murata K, Sugahara K,     Tsuruda K, Masuda M, Takasu N, Kamihira S. Restricted expression of     tumor necrosis factor-related apoptosis-inducing ligand receptor 4     in human peripheral blood lymphocytes. Cell Immunol. 2004;     231(1-2):1-7. -   23. Lim S Y, Raftery M J, Goyette J, Hsu K, Geczy C L. Oxidative     modifications of S100 proteins: functional regulation by redox. J     Leukoc Biol. 2009. -   24. Yamasaki S, Ishikawa E, Sakuma M, Hara H, Ogata K, Saito T.     Mincle is an ITAM-coupled activating receptor that senses damaged     cells. Nat Immunol. 2008; 9(10):1179-1188. -   25. Teixeira V H, Olaso R, Martin-Magniette M L, Lasbleiz S, Jacq L,     Oliveira C R, Hilliquin P, Gut I, Cornelis F, Petit-Teixeira E.     Transcriptome analysis describing new immunity and defense genes in     peripheral blood mononuclear cells of rheumatoid arthritis patients.     PLoS One. 2009; 4(8):e6803. -   26. Chung C P, Oeser A, Raggi P, Gebretsadik T, Shintani A K, Sokka     T, Pincus T, Avalos I, Stein C M. Increased coronary-artery     atherosclerosis in rheumatoid arthritis: relationship to disease     duration and cardiovascular risk factors. Arthritis Rheum. 2005;     52(10):3045-3053. -   27. Cruz-Munoz M E, Dong Z, Shi X, Zhang S, Veillette A. Influence     of CRACC, a SLAM family receptor coupled to the adaptor EAT-2, on     natural killer cell function. Nat Immunol. 2009; 10(3):297-305. -   28. Kim D K, Kabat J, Borrego F, Sanni T B, You C H, Coligan J E.     Human NKG2F is expressed and can associate with DAP12. Mol Immunol.     2004; 41(1):53-62. -   29. Whitman S C, Rateri D L, Szilvassy S J, Yokoyama W, Daugherty A.     Depletion of natural killer cell function decreases atherosclerosis     in low-density lipoprotein receptor null mice. Arterioscler Thromb     Vasc Biol. 2004; 24(6):1049-1054. -   30. Major A S, Fazio S, Linton M F. B-lymphocyte deficiency     increases atherosclerosis in LDL receptor-null mice. Arterioscler     Thromb Vasc Biol. 2002; 22(11):1892-1898. -   31. Robertson A K, Hansson G K. T cells in atherogenesis: for better     or for worse? Arterioscler Thromb Vasc Biol. 2006; 26(11):2421-2432. -   32. Park M Y, Hastie T, Tibshirani R. Averaged gene expressions for     regression. Biostatistics. 2007; 8(2):212-227.

Tables

TABLE 1a Phase 1 and 11 Microarray Cohorts Phase I - CATHGEN Microarray Cohort Phase II - PREDICT Paired Microarray Cohort Controls Cases p. Controls Cases p. Variable (N = 108) (N = 87) value (N = 99) (N = 99) value Sex (% Male) 55 (50.9%) 58 (66.7%) 0.039 75 (75.8%) 75 (75.8%) 0.868 Age (yrs) 55 ± 11 63 ± 10 <.001 55 ± 12 62 ± 11 <.001 Caucasian 56 (51.9%) 60 (69%)   0.023 85 (85.9%) 92 (92.9%) 0.166 BMI 32 ± 7  30 ± 6  0.098 30 ± 7  30 ± 6  0.722 Current Smoker 41 (38%)   45 (51.7%) 0.075 14 (14.1%) 25 (25.3%) 0.074 Systolic BP 144 ± 22  153 ± 25  0.007 132 ± 17  138 ± 18  0.009 Diastolic BP 83 ± 13 87 ± 15 0.077 82 ± 11 80 ± 12 0.271 Hypertension 67 (62%)   65 (74.7%) 0.084 55 (55.6%) 65 (65.7%) 0.191 Dyslipidemia 55 (50.9%) 58 (66.7%) 0.039 50 (50.5%) 69 (69.7%) 0.009 Neutrophil Count 3.8 ± 1.2   4 ± 1.3 0.392 3.9 ± 1.2 4.3 ± 1.5 0.037 Lymphocyte Count 1.8 ± 0.7 1.9 ± 0.7 0.87  2 ± 0.7 1.9 ± 0.6 0.239

TABLE 1b Phase III and IV Algorithm Development and Validation Cohorts Phase III - PREDICT Algorithm Development Cohort Phase IV - PREDICT Algorithm Validation Cohort Controls Cases Controls Cases Variable (N = 410) (N = 230) p. value (N = 334) (N = 192) p. value Sex (% Male) 193 (47.1%) 180 (78.3%) <.001 165 (49.4%) 134 (69.8%) <.001 Age (yrs) 57 ± 12 64 ± 11 <.001 57.7 ± 11.7 64.7 ± 9.8 <.001 Caucasian 347 (84.6%) 210 (91.3%) 0.022 293 (87.7%) 181 (94.3%) 0.015 BMI 31 ± 8  30 ± 6  0.348 31.3 ± 7.0  29.8 ± 5.5 0.010 Current Smoker  87 (21.2%)  45 (19.6%) 0.693  68 (20.4%)  38 (19.8%) 0.703 Systolic BP 133 ± 18  138 ± 18  <.001 132 (18.1)  140 (17.7)  <.001 Diastolic BP 80 ± 12 80 ± 11 0.944 77.5 (10.9)   79.2 (11.3)   0.086 Hypertension 248 (60.5%) 167 (72.6%) 0.003 203 (60.8%) 142 (74.0%) 0.001 Dyslipidemia 225 (54.9%) 170 (73.9%) <.001 208 (62.3%) 133 (69.3%) 0.110 Neutrophil Count  4 ± 1.2 4.3 ± 1.4 0.054 4.0 ± 1.2  4.1 ± 1.3 0.171 Lymphocyte Count  2 ± 0.6 1.9 ± 0.6 0.007 1.9 ± 0.6  1.9 ± 0.6 0.411 Microarray cohorts omit subjects whose array data was excluded based on QC analysis (3 CATHGEN, 12 PREDICT)

TABLE 2 Markers Evaluated by RT-PCR in the Algorithm Development Cohort Micro- Marker Array Cell- Metagene Algorithm Symbol Evidence¹ Type² Cluster Term Term DDX18 3 1.1 SSRP1 3 1.2 CCT2 3 2 1.3 RPL28 N 2 1.4 Norm 2b XIST 2 1, 4, 5 1.5 RASSF7 3 1.6 PKD1 3 1.7 AGPAT5 3 2, 7 1.8 GLS 3 1.9 TMC8 3 1.10 1 3b, 4b RPS4Y1 2 3 1.11 KLF12 3 4 1.12 LCK 2, 3 3, 4, 8 1.13 CD3D 2, 3 3, 4, 8 1.14 1 3b, 4b AES 3 1.15 ZAP70 3 3, 4, 8 1.16 CD81 3 7, 8 1.17 QDPR 3 2, 5 1.18 FXN 2 2 1.19 CORO2A 3 1.20 TCEA1 3 7 1.21 KMO 3 5, 7 2.1 TLR7 3 5 2.2 RHOC 3 2.3 CX3CR1 3 6, 8 2.4 IL11RA 1, 2 3, 4 3.1 IL7R 1, 2, 3 3, 4, 8 3.2 3 FAIM3 2, 3 3, 4, 7 3.3 TCF7 2, 3 3, 4, 8 3.4 3 CD79B 2, 3 7 3.5 2 4a SPIB 2, 3 2, 5, 7 3.6 2 4a CD19 3 5, 7 3.7 BLK 3 5, 7 3.8 PI16 2 3.9 LRRN3 3 3, 4 3.10 4 HNRNPF N 4.1 Norm 5b, 6b TFCP2 N 4.2 Norm 5b, 6b ACBD5 3 4.3 DIAPH1 3 4.4 CD37 3 7 4.5 PLAGL2 3 1 4.6 SRA1 3 5.1 CD300A 2 8 5.2 ELMO2 3 5, 8 5.3 CD33 2 1, 6 6.1 CSPG2 1, 2 6.2 CAT 2 2, 5 6.3 NOD2 1, 3 1, 6 6.4 KCNMB1 2 6.5 5 TCF7L2 3 1, 6, 8 6.6 5 PDK4 3 6.7 5 TBC1D8 3 1, 5, 6 6.8 NR4A1 3 5 7.1 CDKN1C 3 6, 8 7.2 C2 2 7.3 CLC 2 1, 2 8.1 6 OLIG2 2 8.2 ADORA3 2 8.3 6 MMD 1, 2, 3 7 9.1 HIST1H2AE 1, 3 4, 7 9.2 7 AMFR 2 10.1 CD34 N 2 10.2 A_24_P128361 3 11.1 8 5a (AF289562) CD248 2, 3 4 11.2 KLRC4 2 4, 8 12.1 9 3a TARP 2, 3 4, 8 12.2 CCR5 2 4, 5 12.3 CD8A 1 3, 4, 8 12.4 SLAMF7 2 5, 8 12.5 9 3a KLRC2 2 3, 4, 8 12.6 PRSS23 2 8 12.7 NCAM1 N 8 12.8 TNFRSF10C 3 13.1 11 1b IL8RB 1, 3 1, 6, 8 13.2 11 1b TLR4 3 1, 6 13.3 11 1b NAMPT 3 1, 5, 6 13.4 AQP9 3 1, 6 13.5 10 2c S100A8 1, 2, 3 1, 5, 6 13.6 12 2a NCF4 2, 3 1, 6 13.7 10 2c GLT1D1 1, 2, 3 13.8 TXN 2, 3 2, 5 13.9 GABARAPL1 3 13.10 SIRPB2 1, 3 13.11 TRPM6 3 13.12 CD93 1, 2, 3 1, 5, 6 13.13 ASPRV1 3 13.14 ALOX5AP 2, 3 5 13.15 BCL2A1 1, 2, 3 1, 6, 8 13.16 F11R 3 14.1 PTAFR 3 1, 6 14.2 H3F3B 3 7 14.3 TYROBP 2, 3 1, 6, 8 14.4 NCF2 3 1, 5, 6 14.5 KCNE3 2, 3 1, 6 14.6 11 1b LAMP2 2, 3 1 14.7 PLAUR 3 1, 6 14.8 CD14 1 1, 5, 6 14.9 HK3 1, 2 1, 6, 8 14.10 IL18 1 14.11 RGS18 1, 2 1, 6 15.1 BMX 2, 3 16.1 MMP9 2, 3 16.2 S100A12 1, 2, 3 1, 5, 6 16.3 12 2a CLEC4E 2, 3 16.4 12 2a CLEC4D 2, 3 1, 6 16.5 CASP5 2, 3 16.6 13 1a TNFAIP6 2, 3 1 16.7 13 1a IL18RAP 1, 3 3, 4, 8 16.8 13 1a ARG1 2, 3 17.1 14 HP 1 1, 2 17.2 CBS 2, 3 17.3 14 AF161365 3 17.4 15 6a ALAS2 N 18.1 ¹Microarray Evidence: 1 = Wingrove et al, 2 = CATHGEN, 3 = PREDICT, N = normalization Marker ²Cell Type: 1 = CD33+, 2 = CD34+, 3 = CD4+, 4 = CD8+, 5 = Dendritic, 6 = CD14+, 7 = CD19+, 8 = CD56+

TABLE 3 Significance of Clinical Variables in CATHGEN Marker discovery set Clinical Variable p-value Diabetes 0.000560741 Anti Hypertensive Use 0.012462227 HDL 0.088459908 Neutrophil Count 0.129686671 Antidiabetic Use 0.140870844 LDL 0.146873756 Total Cholesterol 0.172382024 WBC Count 0.189994635 Lipid Lowering Agent Use 0.200078333 Triglycerides 0.207728761 Diastolic BP 0.21703689 Chest Pain 0.219704278 Monocyte Count 0.23769698 Platelet Count 0.238534146 Smoker 0.257352165 Lymphocyte Count 0.261169567 Anticoagulant Use 0.321044006 Anti Inflammatory Use 0.332101624 Antiplatelet Use 0.336359859 Statin Use 0.390097042 Calcium Channel Blocker Use 0.401676568 Sex 0.409669446 Postmenopausal 0.418849343 Alcohol Use 0.495208348 NSAID Use 0.536650232 ACE Inhibitor Use 0.687539195 Vasodilator Use 0.715979777 Systolic BP 0.716766737 Antiarrhythmic Use 0.763504492 Salicylates 0.805576705 Beta Blocker Use 0.819779733 Hypertension 0.834786056 Black 0.847458733 Age 0.984504316

TABLE 4 RT-PCR Results on CATHGEN cohort Markers Marker Non-Diabetic p Diabetic p KLRG1 0.933635139 0.000313584 GZMK 0.176629393 0.002075813 CCR5 0.524551866 0.002796076 RPS4Y1 0.641924002 0.003924492 TUBB2A 0.905726045 0.012164059 TARP 0.855579011 0.013579949 IGHA1 0.427023322 0.015653596 CACNA2D2 0.579670417 0.021884775 ADRB2 0.14583996  0.035331896 DB097529 0.739638806 0.037474362 CB853344 0.924313185 0.042530621 RHOH 0.914493918 0.045421079 GPR114 0.113792718 0.082926442 RPS27A 0.127518837 0.085484803 CD3E 0.114159341 0.090230797 RELA 0.800147639 0.124184492 HDC 0.611947115 0.124749411 NR1D1 0.08855384  0.140309177 RRN3 0.883475152 0.14306721 MARCO 0.000742446 0.162858627 ARL17P1 0.009929764 0.163503477 POLR2L 0.110001621 0.169570816 RPL10A 0.372025559 0.176554229 TLR5 5.31034E−05 0.187801635 RPL34 0.047258313 0.194514225 CARKL 0.796426726 0.197876342 DPM3 0.100527185 0.210155758 C11orf2 0.279960963 0.21235462 LIF 0.319291   0.220377076 DHFR 0.005845519 0.227352382 BU540282 0.855833364 0.253041264 CDC42SE2 0.303933209 0.27279888 OLIG2  9.8531E−05 0.291441723 DERL3 0.009989003 0.311630921 SLK 0.022499454 0.315243668 MBOAT2 7.53321E−07 0.32533079 ST3GAL1 0.555439718 0.329090787 FOLR3 0.293485861 0.330960224 NDUFS7 0.510992855 0.362739986 SLC29A1 0.000196258 0.370006714 TCF7 0.139201093 0.384656786 BQ130147 0.005433882 0.39124831 SPSB2 0.710554126 0.392430072 REEP3 0.003636115 0.39572088 CBS 8.54923E−05 0.414841711 GSTO1 0.000439166 0.421164955 VSIG4 0.03654483  0.436274059 OLIG1 0.000739337 0.438928192 RPL8 0.420798397 0.441110854 CR609588 0.829179104 0.44827808 ARG1 9.77852E−05 0.454989416 JAK2 6.14999E−05 0.462535965 CLC 8.43913E−05 0.478209075 PAPSS1 0.002660178 0.497255641 HSPB1 0.011649931 0.503891496 MPZL1 0.069994815 0.504344915 BC032451 0.015738039 0.505628786 BCL2A1 2.81815E−05 0.50979301 CKLF 8.76337E−06 0.515802792 S100A9 1.04727E−07 0.5350388 MAPK8IP1 0.000267919 0.558711324 LOXL2 0.153997075 0.559866641 GSTP1 0.802223179 0.622441442 SLC22A1 0.000127897 0.626928629 HGF 0.001272015 0.63284641 EPOR 0.918974368 0.633466985 ETFB 0.143878666 0.645850919 SSNA1 0.103788889 0.6470392 IRF2 0.018278933 0.665824694 ASMTL 0.311592758 0.681691103 ST6GALNAC3 0.000812432 0.686396961 CSTA  3.1114E−06 0.707081235 SMN1 0.473451351 0.714837746 REEP5 0.000215833 0.733733395 FCGBP 0.074075812 0.796385743 S100A12 4.72256E−06 0.804439181 CAT 4.59232E−08 0.81384176 LOC644246 2.85943E−06 0.820487985 FRAT1 3.39803E−05 0.859050707 ATP11B 6.96563E−05 0.882770629 LGALS1 0.039299421 0.918250705 YWHAZ 0.023358903 0.927846666 MMD 0.153204886 0.941639541 CD33 0.101691174 0.950753885 CD248 0.186672242 0.973814259 ADORA3 0.000150846 0.975200559 TXN 3.22949E−08 0.99228328 LPGAT1 1.58563E−06 0.995574922

TABLE 5 Marker Symbol AA303143 AA601031 ABCC2 ABHD2 ABHD5 ABLIM1 ACO2 ACOX1 ACSL1 ACTB ACVR2B ADA ADNP AF034187 AF085968 AF161353 AF471454 AI276257 AIM1L AK021463 AK022268 AK023663 AK024956 AK056689 AK092942 AK098835 AK124192 ALOX12 ALOX5 ALOX5AP ALS2CR13 AMBN AMFR AMICA1 ANXA2 ANXA3 AOAH AP1S2 APBA2 APBB1 APEH APH1A APOBEC3G APRT AQP2 AQP8 ARG1 ARHGAP24 ARHGAP9 ARHGDIA ARID5B ARPC1B ASCL2 ATG3 ATP1B2 ATP5D ATP6V0B ATP7B AW076051 AW579245 AX721252 AY003763 AY062331 A_23_P158868 A_23_P335398 A_23_P348587 A_23_P44053 A_24_P101960 A_24_P144383 A_24_P221375 A_24_P238427 A_24_P384604 A_24_P417996 A_24_P418712 A_24_P745883 A_24_P84408 A_24_P916228 A_24_P929533 A_32_P28158 A_32_P62137 B2M B4GALT5 BACH2 BAGE BAZ1A BBS2 BC024289 BC031973 BC038432 BC043173 BC062739 BC073935 BCL2A1 BCL3 BCL6 BCL7A BG777521 BI024548 BI026064 BM703463 BMX BOP1 BQ365891 BRF1 BRI3 BST1 BTBD14A BTNL8 BU633383 BX110908 BYSL C10orf54 C11orf2 C12orf35 C14orf156 C15orf38 C16orf24 C16orf57 C1orf96 C20orf24 C20orf3 C20orf77 C2orf39 C6orf129 C6orf32 C7orf34 C8orf31 C9orf19 CALM3 CAMKK2 CAPNS1 CASP4 CASP5 CBS CCDC108 CCDC92 CCL3L3 CCPG1 CD200 CD248 CD302 CD3D CD3E CD5 CD58 CD6 CD7 CD79B CD93 CD96 CDKL5 CDKN1A CEACAM4 CEBPB CEBPD CFLAR CFP CHI3L2 CIB3 CKLF CLEC12A CLEC2D CLEC4D CLEC4E CLIC1 CMTM2 CNTNAP2 COL14A1 COMMD6 COP1 COX6B2 COX6C CPD CR2 CR593845 CR610181 CR613361 CR613944 CREB5 CRIP1 CRISPLD2 CSF2RA CSF2RB CSTA CTBP2 CYB5D2 CYP1A2 CYP4F2 CYP4F3 CYP4F8 DCXR DDX11 DDX3Y DEDD2 DEFA4 DEK DENND3 DHRS3 DHRS7B DHRSX DKFZP434B0335 DKFZp434F142 DKFZp547E087 DOCK10 DOCK8 DOK3 DPF3 DPPA5 DRAP1 DUOX2 DUSP13 DUSP3 DYNLT1 ECH1 ECHDC3 EEF2 EIF1AX EIF2AK2 EIF2C4 EIF4B EIF5A EMP3 EMR3 ENST00000337102 ENST00000360102 ENTPD1 ETS1 EXOC6 EXOSC6 F5 FAIM3 FAM108A1 FAM113B FAM26B FAM44A FAU FBXL5 FCAR FCER1A FGD4 FIBP FKBP5 FKBP9 FLJ22662 FLJ40092 FNDC3B FOS FOXJ1 FOXP1 FPR1 FRAT1 FRAT2 FRS2 FRS3 FTH1 FXYD5 FYB GADD45GIP1 GAMT GBP2 GCA GLRX GLT1D1 GLUL GMFG GNB1 GPA33 GPBAR1 GPC1 GPD1 GPR160 GPR172A GPR37L1 GRB10 GSTT1 GTF2I GYG1 H2AFZ H3F3A HAL HAP1 HDAC4 HDDC2 HDGFL1 HEBP2 HIST1H2AC HIST1H2AJ HIST1H2AM HIST1H2BC HIST2H2AC HLA-DRB5 HLA-E HLA-F HMGB2 HOMER3 HOXB7 HSBP1 HSDL2 HSPA1A HSPB1 HTATIP2 ID2 ID3 IFITM4P IGF2R IGHA1 IGHD IGHM IL13RA1 IL18R1 IL1R2 IL23A IL7R IMPA2 IMPDH1 INCA IRAK3 ISG20 ITM2C JDP2 KCNE3 KCNG1 KCNJ15 KIAA0319L KIAA1430 KIAA1833 KLF6 KLHL3 KLRC4 KSR1 LAG3 LAMP2 LAT2 LCK LHPP LILRA2 LILRB3 LILRP2 LIMS2 LIN7A LIN7B LOC137886 LOC149703 LOC150166 LOC153546 LOC220433 LOC389641 LOC401233 LOC401357 LOC439949 LOC440104 LOC440348 LOC440731 LOC497190 LOC644246 LOXL2 LPGAT1 LRRK2 LSM10 LSM7 LST1 LTBP2 LTBP3 LY96 MACF1 MAGED1 MAGED2 MAGEH1 MAK MAN1C1 MAN2A2 MAP1LC3B MAP3K2 MAP3K3 MAP4K4 MAPK14 MAPK8IP1 MAX MBOAT2 MCL1 MEA1 MEGF10 METTL9 MGAM MGC14425 MLKL MLSTD2 MMD MME MMP9 MNDA MORC3 MOSC1 MOSPD2 MPZL1 MRLC2 MRPL42P5 MRPL53 MSRB2 MST150 MUC20 MUM1 MXD1 MYBPH MYC MYH14 MYL6 MYO15B MYO1F MYO1G NAPSA NAPSB NBPF11 NCF4 NDRG2 NDUFB3 NDUFS8 NFATC1 NFIL3 NGFRAP1 NIN NMI NMT2 NOVA1 NPIP NRBF2 NRIP3 NRP1 NRSN2 NUDT16 OLIG1 OR4C15 OR52B2 OSBPL2 OSBPL6 OSTF1 OXNAD1 PACSIN2 PADI4 PARP1 PDCD7 PDE9A PDK2 PDLIM7 PELI1 PFDN5 PFKFB3 PGD PHB PHC2 PHF5A PHGDH PIK3C2B PIM2 PISD PITPNA PLA2G4A PLA2G7 PLAG1 PLD3 PLEKHA1 PLEKHM1 PLXNC1 POLR2A PPP1R12B PPP4R2 PRAP1 PRKAR1A PRKAR1B PRKCA PRKCD PRKDC PRKY PRSS23 PSMB9 PSMD8 PTEN PTOV1 PTPRCAP PTPRK PTPRM PXK PYCARD PYGL QPCT QPRT RAB24 RAB27A RAB31 RAB32 RABGAP1L RABIF RAC1 RAC2 RAI1 RALB RALGDS RARA RASSF2 RBP7 RCC2 REEP5 REPS2 RFWD2 RGS16 RGS2 RHOG RHOH RIMS4 RIT1 RMND5A RNF130 RNF182 RNF24 ROCK2 ROPN1L RPL17 RPL18A RPL22 RPL31 RPL34 RPL36A RPL37 RPL39 RPS10 RPS15 RPS21 RPS27 RPS27A RPS28 RPS4X RPUSD2 RRN3 RTN3 S100A11 S100A12 S100A8 S100A9 S100P SAMSN1 SAP30 SCRN2 SDCBP SEC14L1 SEC22B SEPX1 SERINC1 SERPINB1 SERPINB8 SERPINE1 SF3B14 SFT2D1 SGCE SH2D5 SLA SLC16A3 SLC1A7 SLC22A15 SLC22A4 SLC25A37 SLC2A10 SLC2A14 SLC2A8 SLC35B4 SLC37A3 SLC40A1 SLC45A2 SLC8A1 SLIT3 SMARCD3 SMC1A SMUG1 SOD2 SP100 SPIB SPRR2C SRM SRPK1 SSBP4 ST6GAL1 STAT5A STC1 STK17B STMN1 STX10 STX3 SULT1B1 SYNCRIP SYT15 TAF9B TALDO1 TANK TARP TAX1BP1 TBCD TBL1XR1 TCEAL1 TCF3 TCF7 THBD TLR2 TLR8 TM7SF2 TMEM102 TMEM48 TMEM49 TMEM68 TMEM86A TNFAIP6 TNFRSF10A TP53I11 TP53TG3 TPST1 TRA@ TRAPPC2L TREM1 TRIB1 TRIM7 TSEN34 TSPAN13 TSPAN16 TSPAN33 TUFM TXN TYROBP U2AF1 UBC UBE2D3 UBE2G2 UBL5 UBQLN1 UCP2 UPF3A URG4 USP11 USP53 USP6 VKORC1 VWCE WDFY3 WDR18 XKR8 XPR1 YOD1 YPEL4 ZBED1 ZCCHC6 ZNF135 ZNF234 ZNF346 ZNF438 ZNF550 ZNF618

TABLE 6 Log odds GOID Ontology Term ratio p value GO:0009987 bp cellular process 0.537 5.55E−19 GO:0002376 bp immune system process 1.728 9.64E−16 GO:0050896 bp response to stimulus 1.118 2.63E−15 GO:0006955 bp immune response 1.796 7.62E−12 GO:0008152 bp metabolic process 0.537 1.64E−09 GO:0065007 bp biological regulation 0.545 2.34E−09 GO:0006952 bp defense response 1.732 1.02E−08 GO:0050789 bp regulation of biological process 0.538 2.16E−08 GO:0043067 bp regulation of programmed cell death 1.508 1.52E−07 GO:0010941 bp regulation of cell death 1.507 1.55E−07 GO:0044238 bp primary metabolic process 0.515 2.05E−07 GO:0007165 bp signal transduction 0.784 2.09E−07 GO:0050794 bp regulation of cellular process 0.520 2.50E−07 GO:0042981 bp regulation of apoptosis 1.493 3.04E−07 GO:0006950 bp response to stress 1.096 3.47E−07 GO:0007154 bp cell communication 0.727 5.29E−07 GO:0045321 bp leukocyte activation 2.190 6.88E−07 GO:0046649 bp lymphocyte activation 2.307 8.27E−07 GO:0044237 bp cellular metabolic process 0.484 4.42E−06 GO:0006690 bp icosanoid metabolic process 3.260 9.29E−06 GO:0001775 bp cell activation 1.968 9.96E−06 GO:0043068 bp positive regulation of programmed cell death 1.746 1.47E−05 GO:0048519 bp negative regulation of biological process 0.976 1.64E−05 GO:0010942 bp positive regulation of cell death 1.737 1.64E−05 GO:0002684 bp positive regulation of immune system process 2.153 2.09E−05 GO:0033559 bp unsaturated fatty acid metabolic process 3.120 2.23E−05 GO:0019538 bp protein metabolic process 0.702 2.24E−05 GO:0002521 bp leukocyte differentiation 2.473 3.07E−05 GO:0006414 bp translational elongation 2.100 3.49E−05 GO:0043065 bp positive regulation of apoptosis 1.706 4.03E−05 GO:0009611 bp response to wounding 1.522 4.38E−05 GO:0009605 bp response to external stimulus 1.260 4.61E−05 GO:0006954 bp inflammatory response 1.781 4.61E−05 GO:0007242 bp intracellular signaling cascade 1.009 6.55E−05 GO:0006917 bp induction of apoptosis 1.843 7.07E−05 GO:0006691 bp leukotriene metabolic process 3.699 7.33E−05 GO:0012502 bp induction of programmed cell death 1.831 7.99E−05 GO:0030098 bp lymphocyte differentiation 2.586 8.30E−05 GO:0002682 bp regulation of immune system process 1.761 9.77E−05 GO:0043449 bp cellular alkene metabolic process 3.603 0.00012 GO:0044267 bp cellular protein metabolic process 0.697 0.00024 GO:0048523 bp negative regulation of cellular process 0.899 0.00046 GO:0042110 bp T cell activation 2.334 0.00051 GO:0050776 bp regulation of immune response 2.018 0.00057 GO:0055114 bp oxidation reduction 1.237 0.00057 GO:0042221 bp response to chemical stimulus 1.082 0.00068 GO:0043066 bp negative regulation of apoptosis 1.625 0.00069 GO:0030097 bp hemopoiesis 1.833 0.00078 GO:0043069 bp negative regulation of programmed cell death 1.608 0.00082 GO:0060548 bp negative regulation of cell death 1.608 0.00082 GO:0002694 bp regulation of leukocyte activation 2.148 0.00083 GO:0043170 bp macromolecule metabolic process 0.431 0.00101 GO:0050865 bp regulation of cell activation 2.114 0.00102 GO:0043412 bp macromolecule modification 0.804 0.00130 GO:0051249 bp regulation of lymphocyte activation 2.174 0.00139 GO:0048583 bp regulation of response to stimulus 1.526 0.00177 GO:0045619 bp regulation of lymphocyte differentiation 2.692 0.00245 GO:0051707 bp response to other organism 1.750 0.00252 GO:0048534 bp hemopoietic or lymphoid organ development 1.673 0.00284 GO:0048518 bp positive regulation of biological process 0.786 0.00285 GO:0002696 bp positive regulation of leukocyte activation 2.297 0.00301 GO:0050867 bp positive regulation of cell activation 2.297 0.00301 GO:0006793 bp phosphorus metabolic process 0.928 0.00377 GO:0006796 bp phosphate metabolic process 0.928 0.00377 GO:0019221 bp cytokine-mediated signaling pathway 2.387 0.00426 GO:0006464 bp protein modification process 0.767 0.00461 GO:0045621 bp positive regulation of lymphocyte 3.046 0.00499 differentiation GO:0002820 bp negative regulation of adaptive immune 3.972 0.00499 response GO:0002823 bp negative regulation of adaptive immune 3.972 0.00499 response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains GO:0044260 bp cellular macromolecule metabolic process 0.413 0.00561 GO:0045580 bp regulation of T cell differentiation 2.724 0.00561 GO:0019370 bp leukotriene biosynthetic process 3.387 0.00561 GO:0043450 bp alkene biosynthetic process 3.387 0.00561 GO:0002520 bp immune system development 1.580 0.00565 GO:0009607 bp response to biotic stimulus 1.477 0.00577 GO:0031347 bp regulation of defense response 2.154 0.00638 GO:0043101 bp purine salvage 4.650 0.00644 GO:0008285 bp negative regulation of cell proliferation 1.413 0.00689 GO:0001817 bp regulation of cytokine production 1.908 0.00710 GO:0016310 bp phosphorylation 0.971 0.00713 GO:0043687 bp post-translational protein modification 0.840 0.00713 GO:0042113 bp B cell activation 2.272 0.00713 GO:0051251 bp positive regulation of lymphocyte activation 2.258 0.00764 GO:0006928 bp cellular component movement 1.253 0.00800 GO:0043433 bp negative regulation of transcription factor 2.902 0.00800 activity GO:0090048 bp negative regulation of transcription regulator 2.902 0.00800 activity GO:0030183 bp B cell differentiation 2.627 0.00807 GO:0002252 bp immune effector process 1.981 0.00816 GO:0050863 bp regulation of T cell activation 2.098 0.00821 GO:0070887 bp cellular response to chemical stimulus 1.562 0.00962 GO:0048522 bp positive regulation of cellular process 0.757 0.00972 GO:0006412 bp translation 1.220 0.01068 GO:0043299 bp leukocyte degranulation 3.650 0.01102 GO:0030091 bp protein repair 4.387 0.01112 GO:0006916 bp anti-apoptosis 1.656 0.01149 GO:0007264 bp small GTPase mediated signal transduction 1.344 0.01149 GO:0042127 bp regulation of cell proliferation 1.035 0.01275 GO:0007243 bp protein kinase cascade 1.332 0.01275 GO:0030217 bp T cell differentiation 2.491 0.01322 GO:0031349 bp positive regulation of defense response 2.491 0.01322 GO:0006468 bp protein amino acid phosphorylation 0.992 0.01363 GO:0002698 bp negative regulation of immune effector 3.557 0.01363 process GO:0043392 bp negative regulation of DNA binding 2.709 0.01508 GO:0043603 bp cellular amide metabolic process 2.449 0.01564 GO:0007166 bp cell surface receptor linked signal transduction 0.727 0.01692 GO:0008625 bp induction of apoptosis via death domain 3.470 0.01716 receptors GO:0009163 bp nucleoside biosynthetic process 4.165 0.01730 GO:0042451 bp purine nucleoside biosynthetic process 4.165 0.01730 GO:0042455 bp ribonucleoside biosynthetic process 4.165 0.01730 GO:0046129 bp purine ribonucleoside biosynthetic process 4.165 0.01730 GO:0033152 bp immunoglobulin V(D)J recombination 4.165 0.01730 GO:0051100 bp negative regulation of binding 2.650 0.01793 GO:0045582 bp positive regulation of T cell differentiation 2.972 0.01808 GO:0006959 bp humoral immune response 2.210 0.01808 GO:0042035 bp regulation of cytokine biosynthetic process 2.210 0.01808 GO:0007162 bp negative regulation of cell adhesion 2.539 0.02085 GO:0051591 bp response to cAMP 3.235 0.02085 GO:0042036 bp negative regulation of cytokine biosynthetic 3.235 0.02085 process GO:0045727 bp positive regulation of translation 3.235 0.02085 GO:0051098 bp regulation of binding 1.839 0.02085 GO:0051101 bp regulation of DNA binding 1.984 0.02085 GO:0032944 bp regulation of mononuclear cell proliferation 2.309 0.02085 GO:0050670 bp regulation of lymphocyte proliferation 2.309 0.02085 GO:0070663 bp regulation of leukocyte proliferation 2.309 0.02085 GO:0045581 bp negative regulation of T cell differentiation 3.972 0.02085 GO:0002703 bp regulation of leukocyte mediated immunity 2.513 0.02085 GO:0002706 bp regulation of lymphocyte mediated immunity 2.594 0.02085 GO:0018193 bp peptidyl-amino acid modification 1.779 0.02085 GO:0019321 bp pentose metabolic process 3.972 0.02085 GO:0045055 bp regulated secretory pathway 3.235 0.02085 GO:0010310 bp regulation of hydrogen peroxide metabolic 3.972 0.02085 process GO:0002822 bp regulation of adaptive immune response 2.487 0.02155 based on somatic recombination of immune receptors built from immunoglobulin superfamily domains GO:0019748 bp secondary metabolic process 2.079 0.02180 GO:0006631 bp fatty acid metabolic process 1.541 0.02416 GO:0046688 bp response to copper ion 3.802 0.02497 GO:0045628 bp regulation of T-helper 2 cell differentiation 3.802 0.02497 GO:0002704 bp negative regulation of leukocyte mediated 3.802 0.02497 immunity GO:0002707 bp negative regulation of lymphocyte mediated 3.802 0.02497 immunity GO:0046456 bp icosanoid biosynthetic process 2.724 0.02597 GO:0010033 bp response to organic substance 1.157 0.02601 GO:0080134 bp regulation of response to stress 1.524 0.02611 GO:0042180 bp cellular ketone metabolic process 1.016 0.02815 GO:0002712 bp regulation of B cell mediated immunity 3.098 0.02864 GO:0002889 bp regulation of immunoglobulin mediated 3.098 0.02864 immune response GO:0002819 bp regulation of adaptive immune response 2.387 0.02920 GO:0008624 bp induction of apoptosis by extracellular signals 2.387 0.02920 GO:0050778 bp positive regulation of immune response 1.864 0.02930 GO:0030031 bp cell projection assembly 1.998 0.02943 GO:0002443 bp leukocyte mediated immunity 1.998 0.02943 GO:0051250 bp negative regulation of lymphocyte activation 2.650 0.03116 GO:0000122 bp negative regulation of transcription from RNA 1.843 0.03194 polymerase II promoter GO:0043094 bp cellular metabolic compound salvage 3.650 0.03249 GO:0045749 bp negative regulation of S phase of mitotic cell 3.650 0.03249 cycle GO:0050777 bp negative regulation of immune response 3.034 0.03249 GO:0080010 bp regulation of oxygen and reactive oxygen 3.650 0.03249 species metabolic process GO:0006968 bp cellular defense response 2.131 0.03328 GO:0045087 bp innate immune response 1.716 0.03404 GO:0006739 bp NADP metabolic process 2.972 0.03786 GO:0045088 bp regulation of innate immune response 2.580 0.03789 GO:0002697 bp regulation of immune effector process 2.082 0.04021 GO:0009617 bp response to bacterium 1.783 0.04172 GO:0006636 bp unsaturated fatty acid biosynthetic process 2.546 0.04198 GO:0006101 bp citrate metabolic process 3.513 0.04231 GO:0002828 bp regulation of T-helper 2 type immune 3.513 0.04231 response GO:0046777 bp protein amino acid autophosphorylation 2.066 0.04231 GO:0060263 bp regulation of respiratory burst 3.513 0.04231 GO:0006082 bp organic acid metabolic process 0.971 0.04277 GO:0019752 bp carboxylic acid metabolic process 0.980 0.04277 GO:0043436 bp oxoacid metabolic process 0.980 0.04277 GO:0009058 bp biosynthetic process 0.423 0.04277 GO:0030155 bp regulation of cell adhesion 1.763 0.04277 GO:0050870 bp positive regulation of T cell activation 2.034 0.04277 GO:0050727 bp regulation of inflammatory response 2.228 0.04277 GO:0007265 bp Ras protein signal transduction 1.659 0.04277 GO:0010629 bp negative regulation of Marker expression 1.186 0.04277 GO:0042742 bp defense response to bacterium 2.018 0.04277 GO:0002695 bp negative regulation of leukocyte activation 2.481 0.04277 GO:0009146 bp purine nucleoside triphosphate catabolic 3.387 0.04455 process GO:0045620 bp negative regulation of lymphocyte 3.387 0.04455 differentiation GO:0050869 bp negative regulation of B cell activation 3.387 0.04455 GO:0050853 bp B cell receptor signaling pathway 3.387 0.04455 GO:0050864 bp regulation of B cell activation 2.449 0.04504 GO:0018212 bp peptidyl-tyrosine modification 2.449 0.04504 GO:0048584 bp positive regulation of response to stimulus 1.526 0.04534 GO:0019079 bp viral genome replication 2.802 0.04639 GO:0009892 bp negative regulation of metabolic process 0.997 0.04764 GO:0006357 bp regulation of transcription from RNA 0.950 0.04827 polymerase II promoter GO:0010605 bp negative regulation of macromolecule 1.020 0.04827 metabolic process GO:0010558 bp negative regulation of macromolecule 1.106 0.04890 biosynthetic process GO:0005623 cc cell 0.465 2.15E−24 GO:0044464 cc cell part 0.465 2.15E−24 GO:0005622 cc intracellular 0.434 1.13E−11 GO:0044424 cc intracellular part 0.430 1.26E−10 GO:0005737 cc cytoplasm 0.537 7.50E−10 GO:0016020 cc membrane 0.551 1.18E−08 GO:0005829 cc cytosol 1.207 2.30E−07 GO:0005886 cc plasma membrane 0.744 7.43E−07 GO:0043229 cc intracellular organelle 0.400 1.46E−06 GO:0043226 cc organelle 0.399 1.54E−06 GO:0016021 cc integral to membrane 0.583 4.13E−06 GO:0044444 cc cytoplasmic part 0.583 5.65E−06 GO:0031224 cc intrinsic to membrane 0.559 1.09E−05 GO:0044425 cc membrane part 0.516 1.44E−05 GO:0022626 cc cytosolic ribosome 2.173 0.00034 GO:0044445 cc cytosolic part 1.912 0.00040 GO:0043231 cc intracellular membrane-bounded organelle 0.339 0.00111 GO:0043227 cc membrane-bounded organelle 0.339 0.00116 GO:0033279 cc ribosomal subunit 1.837 0.00223 GO:0022627 cc cytosolic small ribosomal subunit 2.532 0.00501 GO:0044459 cc plasma membrane part 0.659 0.00975 GO:0043228 cc non-membrane-bounded organelle 0.588 0.00986 GO:0043232 cc intracellular non-membrane-bounded 0.588 0.00986 organelle GO:0044422 cc organelle part 0.435 0.01029 GO:0044446 cc intracellular organelle part 0.433 0.01149 GO:0005887 cc integral to plasma membrane 0.777 0.01842 GO:0031226 cc intrinsic to plasma membrane 0.751 0.02085 GO:0032991 cc macromolecular complex 0.516 0.02085 GO:0005840 cc ribosome 1.329 0.02085 GO:0005634 cc nucleus 0.348 0.02601 GO:0016461 cc unconventional myosin complex 3.650 0.03249 GO:0015935 cc small ribosomal subunit 1.960 0.03404 GO:0005488 mf binding 0.469 0.00000 GO:0005515 mf protein binding 0.601 0.00000 GO:0003824 mf catalytic activity 0.554 0.00000 GO:0003823 mf antigen binding 3.025 0.00051 GO:0046983 mf protein dimerization activity 1.350 0.00085 GO:0004871 mf signal transducer activity 0.738 0.00223 GO:0060089 mf molecular transducer activity 0.738 0.00223 GO:0004197 mf cysteine-type endopeptidase activity 2.223 0.00456 GO:0016491 mf oxidoreductase activity 1.060 0.00553 GO:0004872 mf receptor activity 0.763 0.00990 GO:0017070 mf U6 snRNA binding 4.387 0.01112 GO:0005536 mf glucose binding 4.387 0.01112 GO:0016208 mf AMP binding 3.018 0.01639 GO:0030234 mf enzyme regulator activity 0.895 0.01730 GO:0008113 mf peptide-methionine-(S)-S-oxide reductase 4.165 0.01730 activity GO:0043169 mf cation binding 0.420 0.01977 GO:0043167 mf ion binding 0.408 0.02085 GO:0046872 mf metal ion binding 0.419 0.02085 GO:0005529 mf sugar binding 1.586 0.02085 GO:0016165 mf lipoxygenase activity 3.972 0.02085 GO:0047485 mf protein N-terminus binding 2.291 0.02085 GO:0005509 mf calcium ion binding 0.845 0.02138 GO:0030528 mf transcription regulator activity 0.733 0.02364 GO:0001848 mf complement binding 3.802 0.02497 GO:0005527 mf macrolide binding 3.802 0.02497 GO:0005528 mf FK506 binding 3.802 0.02497 GO:0019838 mf growth factor binding 1.776 0.02637 GO:0003735 mf structural constituent of ribosome 1.348 0.02664 GO:0019210 mf kinase inhibitor activity 2.412 0.02732 GO:0005198 mf structural molecule activity 0.901 0.02868 GO:0019899 mf enzyme binding 1.176 0.02868 GO:0005351 mf sugar:hydrogen symporter activity 2.387 0.02920 GO:0005402 mf cation:sugar symporter activity 2.387 0.02920 GO:0004672 mf protein kinase activity 0.930 0.02979 GO:0004888 mf transmembrane receptor activity 0.813 0.02981 GO:0019207 mf kinase regulator activity 1.853 0.03047 GO:0015144 mf carbohydrate transmembrane transporter 2.317 0.03624 activity GO:0051119 mf sugar transmembrane transporter activity 2.317 0.03624 GO:0003700 mf transcription factor activity 0.928 0.04231 GO:0004859 mf phospholipase inhibitor activity 3.513 0.04231 GO:0019887 mf protein kinase regulator activity 1.885 0.04277 GO:0004896 mf cytokine receptor activity 2.050 0.04277 GO:0008603 mf cAMP-dependent protein kinase regulator 2.857 0.04277 activity GO:0015295 mf solute:hydrogen symporter activity 2.250 0.04277 GO:0055102 mf lipase inhibitor activity 3.387 0.04455 GO:0016840 mf carbon-nitrogen lyase activity 3.387 0.04455 GO:0016671 mf oxidoreductase activity, acting on sulfur group 3.387 0.04455 of donors, disulfide as acceptor GO:0000166 mf nucleotide binding 0.514 0.04639 GO:0016787 mf hydrolase activity 0.502 0.04653

TABLE 7 Number of AUC Ridge AUC Logistic AUC Ridge AUC Logistic Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Terms Regression Regression Regression Regression TRUE TRUE TRUE TRUE TRUE TRUE TRUE 7 0.781188 0.785011 0.781188 0.785011 TRUE TRUE TRUE TRUE TRUE TRUE FALSE 6 0.775592 0.779372 Mean 0.775346 0.778701 TRUE TRUE TRUE TRUE TRUE FALSE TRUE 6 0.764453 0.768001 SD 0.00497 0.004876 TRUE TRUE TRUE TRUE FALSE TRUE TRUE 6 0.778834 0.781621 TRUE TRUE TRUE FALSE TRUE TRUE TRUE 6 0.777071 0.781157 TRUE TRUE FALSE TRUE TRUE TRUE TRUE 6 0.778887 0.782202 TRUE FALSE TRUE TRUE TRUE TRUE TRUE 6 0.776268 0.779626 FALSE TRUE TRUE TRUE TRUE TRUE TRUE 6 0.776321 0.778929

TABLE 8 AUC Ridge AUC Logistic Term Regression Regression 7 0.781188 0.785011 6 0.775346 0.778701 5 0.769504 0.772411 4 0.763641 0.766090 3 0.757567 0.759481 2 0.751191 0.752634 1 0.744420 0.745125 0 0.736732 0.736732 DF Model 0.677495 ± .042437

TABLE 9 Number of AUC Ridge AUC Logistic AUC Ridge AUC Logistic Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Terms Regression Regression Regression Regression TRUE TRUE TRUE TRUE TRUE FALSE FALSE 5 0.756671 0.761212 Mean 0.769504 0.772411 TRUE TRUE TRUE TRUE FALSE TRUE FALSE 5 0.772815 0.774463 SD 0.006329 0.006033 TRUE TRUE TRUE FALSE TRUE TRUE FALSE 5 0.771306 0.775846 TRUE TRUE FALSE TRUE TRUE TRUE FALSE 5 0.774389 0.777155 TRUE FALSE TRUE TRUE TRUE TRUE FALSE 5 0.772889 0.775508 FALSE TRUE TRUE TRUE TRUE TRUE FALSE 5 0.770334 0.772668 TRUE TRUE TRUE TRUE FALSE FALSE TRUE 5 0.761507 0.763841 TRUE TRUE TRUE FALSE TRUE FALSE TRUE 5 0.759744 0.764443 TRUE TRUE FALSE TRUE TRUE FALSE TRUE 5 0.762542 0.765424 TRUE FALSE TRUE TRUE TRUE FALSE TRUE 5 0.760124 0.76345 FALSE TRUE TRUE TRUE TRUE FALSE TRUE 5 0.760684 0.763249 TRUE TRUE TRUE FALSE FALSE TRUE TRUE 5 0.7748 0.77819 TRUE TRUE FALSE TRUE FALSE TRUE TRUE 5 0.776659 0.779921 TRUE FALSE TRUE TRUE FALSE TRUE TRUE 5 0.773523 0.775466 FALSE TRUE TRUE TRUE FALSE TRUE TRUE 5 0.773766 0.775529 TRUE TRUE FALSE FALSE TRUE TRUE TRUE 5 0.775835 0.779731 TRUE FALSE TRUE FALSE TRUE TRUE TRUE 5 0.771633 0.775613 FALSE TRUE TRUE FALSE TRUE TRUE TRUE 5 0.771084 0.773702 TRUE FALSE FALSE TRUE TRUE TRUE TRUE 5 0.775191 0.776279 FALSE TRUE FALSE TRUE TRUE TRUE TRUE 5 0.773914 0.776289 FALSE FALSE TRUE TRUE TRUE TRUE TRUE 5 0.770165 0.772657

TABLE 10 Number of AUC Ridge AUC Logistic AUC Ridge AUC Logistic Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Terms Regression Regression Regression Regression TRUE TRUE TRUE TRUE FALSE FALSE FALSE 4 0.753409 0.755542 Mean 0.763641 0.76609 TRUE TRUE TRUE FALSE TRUE FALSE FALSE 4 0.752269 0.757854 SD 0.007095 0.00666 TRUE TRUE FALSE TRUE TRUE FALSE FALSE 4 0.75475 0.759132 TRUE FALSE TRUE TRUE TRUE FALSE FALSE 4 0.754644 0.758973 FALSE TRUE TRUE TRUE TRUE FALSE FALSE 4 0.752923 0.755964 TRUE TRUE TRUE FALSE FALSE TRUE FALSE 4 0.769014 0.771379 TRUE TRUE FALSE TRUE FALSE TRUE FALSE 4 0.772245 0.772932 TRUE FALSE TRUE TRUE FALSE TRUE FALSE 4 0.769289 0.770514 FALSE TRUE TRUE TRUE FALSE TRUE FALSE 4 0.76742 0.768296 TRUE TRUE FALSE FALSE TRUE TRUE FALSE 4 0.770746 0.7748 TRUE FALSE TRUE FALSE TRUE TRUE FALSE 4 0.768423 0.771601 FALSE TRUE TRUE FALSE TRUE TRUE FALSE 4 0.765129 0.768434 TRUE FALSE FALSE TRUE TRUE TRUE FALSE 4 0.771042 0.773185 FALSE TRUE FALSE TRUE TRUE TRUE FALSE 4 0.769152 0.771116 FALSE FALSE TRUE TRUE TRUE TRUE FALSE 4 0.767262 0.768592 TRUE TRUE TRUE FALSE FALSE FALSE TRUE 4 0.75684 0.759332 TRUE TRUE FALSE TRUE FALSE FALSE TRUE 4 0.759744 0.762014 TRUE FALSE TRUE TRUE FALSE FALSE TRUE 4 0.75722 0.75872 FALSE TRUE TRUE TRUE FALSE FALSE TRUE 4 0.757622 0.759311 TRUE TRUE FALSE FALSE TRUE FALSE TRUE 4 0.758392 0.76194 TRUE FALSE TRUE FALSE TRUE FALSE TRUE 4 0.754232 0.757463 FALSE TRUE TRUE FALSE TRUE FALSE TRUE 4 0.754739 0.758625 TRUE FALSE FALSE TRUE TRUE FALSE TRUE 4 0.757696 0.76081 FALSE TRUE FALSE TRUE TRUE FALSE TRUE 4 0.759448 0.760821 FALSE FALSE TRUE TRUE TRUE FALSE TRUE 4 0.756228 0.757865 TRUE TRUE FALSE FALSE FALSE TRUE TRUE 4 0.773956 0.777345 TRUE FALSE TRUE FALSE FALSE TRUE TRUE 4 0.768824 0.771253 FALSE TRUE TRUE FALSE FALSE TRUE TRUE 4 0.768391 0.770482 TRUE FALSE FALSE TRUE FALSE TRUE TRUE 4 0.77252 0.773998 FALSE TRUE FALSE TRUE FALSE TRUE TRUE 4 0.772277 0.77365 FALSE FALSE TRUE TRUE FALSE TRUE TRUE 4 0.767536 0.769299 TRUE FALSE FALSE FALSE TRUE TRUE TRUE 4 0.770376 0.77328 FALSE TRUE FALSE FALSE TRUE TRUE TRUE 4 0.769743 0.77196 FALSE FALSE TRUE FALSE TRUE TRUE TRUE 4 0.764875 0.767167 FALSE FALSE FALSE TRUE TRUE TRUE TRUE 4 0.769057 0.769489

TABLE 11 Number of AUC Ridge AUC Logistic AUC Ridge AUC Logistic Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Terms Regression Regression Regression Regression TRUE TRUE TRUE FALSE FALSE FALSE FALSE 3 0.749217 0.752026 Mean 0.757567 0.759481 TRUE TRUE FALSE TRUE FALSE FALSE FALSE 3 0.751783 0.754158 SD 0.007376 0.006937 TRUE FALSE TRUE TRUE FALSE FALSE FALSE 3 0.750474 0.752448 FALSE TRUE TRUE TRUE FALSE FALSE FALSE 3 0.749492 0.749734 TRUE TRUE FALSE FALSE TRUE FALSE FALSE 3 0.751223 0.755732 TRUE FALSE TRUE FALSE TRUE FALSE FALSE 3 0.748985 0.752913 FALSE TRUE TRUE FALSE TRUE FALSE FALSE 3 0.746725 0.750442 TRUE FALSE FALSE TRUE TRUE FALSE FALSE 3 0.751804 0.755362 FALSE TRUE FALSE TRUE TRUE FALSE FALSE 3 0.750864 0.753388 FALSE FALSE TRUE TRUE TRUE FALSE FALSE 3 0.74946 0.752047 TRUE TRUE FALSE FALSE FALSE TRUE FALSE 3 0.76856 0.770535 TRUE FALSE TRUE FALSE FALSE TRUE FALSE 3 0.765245 0.766628 FALSE TRUE TRUE FALSE FALSE TRUE FALSE 3 0.762711 0.764052 TRUE FALSE FALSE TRUE FALSE TRUE FALSE 3 0.767916 0.768687 FALSE TRUE FALSE TRUE FALSE TRUE FALSE 3 0.766618 0.766702 FALSE FALSE TRUE TRUE FALSE TRUE FALSE 3 0.763767 0.764432 TRUE FALSE FALSE FALSE TRUE TRUE FALSE 3 0.767388 0.770134 FALSE TRUE FALSE FALSE TRUE TRUE FALSE 3 0.76458 0.766734 FALSE FALSE TRUE FALSE TRUE TRUE FALSE 3 0.760884 0.762669 FALSE FALSE FALSE TRUE TRUE TRUE FALSE 3 0.766005 0.766618 TRUE TRUE FALSE FALSE FALSE FALSE TRUE 3 0.756133 0.758456 TRUE FALSE TRUE FALSE FALSE FALSE TRUE 3 0.751213 0.752891 FALSE TRUE TRUE FALSE FALSE FALSE TRUE 3 0.751899 0.753747 TRUE FALSE FALSE TRUE FALSE FALSE TRUE 3 0.755404 0.75702 FALSE TRUE FALSE TRUE FALSE FALSE TRUE 3 0.756904 0.758213 FALSE FALSE TRUE TRUE FALSE FALSE TRUE 3 0.753166 0.753082 TRUE FALSE FALSE FALSE TRUE FALSE TRUE 3 0.75268 0.756143 FALSE TRUE FALSE FALSE TRUE FALSE TRUE 3 0.753155 0.755996 FALSE FALSE TRUE FALSE TRUE FALSE TRUE 3 0.748932 0.750484 FALSE FALSE FALSE TRUE TRUE FALSE TRUE 3 0.753103 0.755119 TRUE FALSE FALSE FALSE FALSE TRUE TRUE 3 0.767969 0.77063 FALSE TRUE FALSE FALSE FALSE TRUE TRUE 3 0.767832 0.76969 FALSE FALSE TRUE FALSE FALSE TRUE TRUE 3 0.762099 0.763112 FALSE FALSE FALSE TRUE FALSE TRUE TRUE 3 0.766544 0.76723 FALSE FALSE FALSE FALSE TRUE TRUE TRUE 3 0.764105 0.76458

TABLE 12 Number of AUC Ridge AUC Logistic AUC Ridge AUC Logistic Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Terms Regression Regression Regression Regression TRUE TRUE FALSE FALSE FALSE FALSE FALSE 2 0.748341 0.750526 Mean 0.751191 0.752634 TRUE FALSE TRUE FALSE FALSE FALSE FALSE 2 0.745247 0.747485 SD 0.007213 0.006971 FALSE TRUE TRUE FALSE FALSE FALSE FALSE 2 0.743589 0.744508 TRUE FALSE FALSE TRUE FALSE FALSE FALSE 2 0.747945 0.749254 FALSE TRUE FALSE TRUE FALSE FALSE FALSE 2 0.748203 0.748151 FALSE FALSE TRUE TRUE FALSE FALSE FALSE 2 0.745722 0.745268 TRUE FALSE FALSE FALSE TRUE FALSE FALSE 2 0.74682 0.75117 FALSE TRUE FALSE FALSE TRUE FALSE FALSE 2 0.745522 0.748225 FALSE FALSE TRUE FALSE TRUE FALSE FALSE 2 0.742333 0.74455 FALSE FALSE FALSE TRUE TRUE FALSE FALSE 2 0.746968 0.749618 TRUE FALSE FALSE FALSE FALSE TRUE FALSE 2 0.764516 0.765794 FALSE TRUE FALSE FALSE FALSE TRUE FALSE 2 0.762225 0.762563 FALSE FALSE TRUE FALSE FALSE TRUE FALSE 2 0.75759 0.758561 FALSE FALSE FALSE TRUE FALSE TRUE FALSE 2 0.762415 0.762985 FALSE FALSE FALSE FALSE TRUE TRUE FALSE 2 0.760145 0.761486 TRUE FALSE FALSE FALSE FALSE FALSE TRUE 2 0.750526 0.752057 FALSE TRUE FALSE FALSE FALSE FALSE TRUE 2 0.751012 0.752701 FALSE FALSE TRUE FALSE FALSE FALSE TRUE 2 0.74568 0.746092 FALSE FALSE FALSE TRUE FALSE FALSE TRUE 2 0.750811 0.751888 FALSE FALSE FALSE FALSE TRUE FALSE TRUE 2 0.747739 0.749608 FALSE FALSE FALSE FALSE FALSE TRUE TRUE 2 0.761655 0.762827

TABLE 13 Number of AUC Ridge AUC Logistic AUC Ridge AUC Logistic Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Terms Regression Regression Regression Regression TRUE FALSE FALSE FALSE FALSE FALSE FALSE 1 0.742993 0.744978 Mean 0.74442 0.745125 FALSE TRUE FALSE FALSE FALSE FALSE FALSE 1 0.742555 0.743273 SD 0.006498 0.006455 FALSE FALSE TRUE FALSE FALSE FALSE FALSE 1 0.738732 0.738437 FALSE FALSE FALSE TRUE FALSE FALSE FALSE 1 0.743288 0.743288 FALSE FALSE FALSE FALSE TRUE FALSE FALSE 1 0.740939 0.742903 FALSE FALSE FALSE FALSE FALSE TRUE FALSE 1 0.757125 0.757442 FALSE FALSE FALSE FALSE FALSE FALSE TRUE 1 0.74531 0.745553

TABLE 14 Number of AUC Ridge AUC Logistic AUC Ridge AUC Logistic Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Terms Regression Regression Regression Regression FALSE FALSE FALSE FALSE FALSE FALSE FALSE 0 0.736732 0.736732 0.736732 0.736732

TABLE 15 Algorithm Substitute AUC Ridge AUC Logistic RefSeq Algorithm RefSeq Substitute Marker Marker Correlation Regression egression Marker Marker S100A12 MMP9 0.77 0.781 0.784 NM_005621 NM_004994 CLEC4E ALOX5AP 0.74 0.780 0.783 NM_014358 NM_001629 S100A8 NAMPT 0.90 0.781 0.786 NM_002964 NM_005746 CASP5 H3F3B 0.63 0.783 0.787 NM_001136112 NM_005324 IL18RAP TXN 0.52 0.774 0.778 NM_003853 NM_003329 TNFAIP6 PLAUR 0.66 0.779 0.783 NM_007115 NM_001005376 AQP9 GLT1D1 0.93 0.781 0.785 NM_020980 NM_144669 NCF4 NCF2 0.91 0.780 0.784 NM_000631 NM_000433 CD3D LCK 0.95 0.779 0.784 NM_000732 NM_001042771 TMC8 CCT2 0.85 0.781 0.785 NM_152468 NM_006431 CD79B CD19 0.95 0.796 0.809 NM_000626 NM_001770 SPIB BLK 0.89 0.780 0.784 NM_003121 NM_001715 HNRPF ACBD5 0.88 0.779 0.783 NM_001098204 NM_001042473 TFCP2 DDX18 0.88 0.781 0.786 NM_005653 NM_006773 RPL28 SSRP1 0.91 0.782 0.786 NM_000991 NM_003146 AF161365 AF161365 1.00 0.781 0.785 AF161365 AF161365 AF289562 CD248 0.53 0.779 0.783 AF289562 NM_020404 SLAMF7 CX3CR1 0.83 0.778 0.783 NM_021181 NM_001171171 KLRC4 CD8A 0.79 0.794 0.805 NM_013431 NM_001145873 IL8RB BCL2A1 0.82 0.780 0.785 NM_001557 NM_001114735 TNFRSF10C PTAFR 0.84 0.781 0.785 NM_003841 NM_000952 KCNE3 LAMP2 0.90 0.781 0.784 NM_005472 NM_001122606 TLR4 TYROBP 0.84 0.780 0.783 NM_138554 NM_003332 Mean 0.82 0.781 0.786 SD 0.13 0.005 0.007 Markers are replaced with the most highly correlated non-algorithm marker in the PCR data set, while ensuring that the set of Substitute Markers has no duplicates.

TABLE 16 AUC Ridge AUC Logistic Genomic Terms Regression Regression Full Model 0.781 0.785 1 Marker Replaced 0.781 ± .009 0.786 ± .014 5 Markers Replaced 0.781 ± .014 0.788 ± .021 10 Markers Replaced 0.778 ± .015 0.785 ± .020 15 Markers Replaced 0.779 ± .014 0.787 ± .020 20 Markers Replaced 0.771 ± .010 0.779 ± .013 All Markers Replaced 0.770 0.775 DF Model 0.677 ± .042 For the 5, 10, 15, 20 Markers replaced analyses, markers were selected at random 100 times for each of the analyses.

TABLE 17 Delta AUC Delta AUC Ridge Logistic Markers Regression Regression Markers Term Type Predictive AF161365.HNRPF.TFCP2 0 0 3 1 Original Yes AF161365.TFCP2 0.003495 0.004097 2 1 Original Yes AF161365.HNRPF −0.00327 −0.00391 2 1 Original Yes AF161365.ACBD5.DDX18 0.00473 0.004762 3 1 Substitute Yes AF161365.DDX18 4.22E−05 0.000243 2 1 Substitute Yes AF161365.ACBD5 −0.00278 −0.0031 2 1 Substitute Yes

TABLE 18 Delta AUC Delta AUC Ridge Logistic Markers Regression Regression Markers Term Type Predictive AF289562.HNRPF.TFCP2 0 0 3 2 Original Yes AF289562.TFCP2 −6.34E−05 −0.00073 2 2 Original Yes AF289562.HNRPF 0.000549 0.000306 2 2 Original Yes CD248.ACBD5.DDX18 −0.00505 −0.00625 3 2 Substitute Yes CD248.DDX18 −0.00535 −0.00654 2 2 Substitute Yes CD248.ACBD5 −0.00506 −0.00588 2 2 Substitute Yes

TABLE 19 Delta AUC Delta AUC Ridge Logistic Markers Regression Regression Markers Term Predictive CD3D.TMC8.CD79B.SPIB 0 0 4 3 Yes CD3D.CD79B.SPIB −0.00039 −0.00056 3 3 Yes TMC8.CD79B.SPIB 0 9.50E−05 3 3 Yes CD3D.TMC8.CD79B 0.000612 0.000697 3 3 Yes CD3D.TMC8.SPIB −0.00053 −0.00041 3 3 Yes CD3D.CD79B −0.00016 8.45E−05 2 3 Yes CD3D.SPIB −0.00058 −0.0008 2 3 Yes TMC8.CD79B 0.00038 0.000676 2 3 Yes TMC8.SPIB −0.00048 −0.00023 2 3 Yes LCK.CCT2.CD19.BLK −0.00846 −0.00441 4 3 No LCK.CD19.BLK −0.00838 −0.00536 3 3 No CCT2.CD19.BLK −0.00729 −0.00463 3 3 No LCK.CCT2.CD19 −0.00619 −0.00338 3 3 No LCK.CCT2.BLK −0.00043 −0.00016 3 3 Yes LCK.CD19 −0.00692 −0.00316 2 3 No LCK.BLK −0.00027 −8.45E−05  2 3 Yes CCT2.CD19 −0.00605 −0.0017 2 3 No CCT2.BLK −0.00036 −0.00012 2 3 Yes CD3D.CD79B −0.00016 8.45E−05 2 3 Yes CD3D.SPIB −0.00058 −0.0008 2 3 Yes CD3D.CD19 −0.00729 −0.00222 2 3 No CD3D.BLK −0.00058 −0.00034 2 3 Yes TMC8.CD79B 0.00038 0.000676 2 3 Yes TMC8.SPIB −0.00048 −0.00023 2 3 Yes TMC8.CD19 −0.00561 −0.00134 2 3 Mixed TMC8.BLK −0.00042 −0.0007 2 3 Yes LCK.CD79B −0.00073 −0.00045 2 3 Yes LCK.SPIB −0.00109 −0.00118 2 3 Yes LCK.CD19 −0.00692 −0.00316 2 3 No LCK.BLK −0.00027 −8.45E−05  2 3 Yes CCT2.CD79B 0.000106 0.000116 2 3 Yes CCT2.SPIB −0.00057 −0.0007 2 3 Yes CCT2.CD19 −0.00605 −0.0017 2 3 No CCT2.BLK −0.00036 −0.00012 2 3 Yes

TABLE 20 Delta AUC Delta AUC Ridge Logistic Markers Regression Regression Markers Term Type Predictive S100A12.CLEC4E.S100A8.RPL28 0 0 4 4 Original Yes S100A12.CLEC4E.RPL28 −0.00079 −0.00079 3 4 Original Yes S100A12.S100A8.RPL28 −0.00068 −0.00068 3 4 Original Yes CLEC4E.S100A8.RPL28 0.000528 0.000528 3 4 Original Yes S100A12.RPL28 −0.00166 −0.00166 2 4 Original Yes CLEC4E.RPL28 −0.00183 −0.00183 2 4 Original Yes S100A8.RPL28 0.000538 0.000538 2 4 Original Yes MMP9.ALOX5AP.NAMPT.SSRP1 −0.0003 −0.0003 4 4 Substitute Yes MMP9.ALOX5AP.SSRP1 −0.00082 −0.00082 3 4 Substitute Yes MMP9.NAMPT.SSRP1 −0.00052 −0.00052 3 4 Substitute Yes ALOX5AP.NAMPT.SSRP1 0.000169 0.000169 3 4 Substitute Yes MMP9.SSRP1 −0.00186 −0.00186 2 4 Substitute Yes ALOX5AP.SSRP1 −3.17E−05 −3.17E−05 2 4 Substitute Yes NAMPT.SSRP1 −0.0002 −0.0002 2 4 Substitute Yes

TABLE 21 Delta AUC Delta AUC Ridge Logistic Markers Regression Regression Markers Term Predictive S100A12.CLEC4E.S100A8.AQP9.NCF4 0 0 5 5 Yes S100A12.CLEC4E.AQP9.NCF4 −0.00021 0.000317 4 5 Yes S100A12.S100A8.AQP9.NCF4 −0.00173 −0.00269 4 5 Yes CLEC4E.S100A8.AQP9.NCF4 0.001014 0.001499 4 5 Yes S100A12.CLEC4E.S100A8.AQP9 −0.00091 −0.00105 4 5 Yes S100A12.CLEC4E.S100A8.NCF4 0.000348 −0.00013 4 5 Yes S100A12.AQP9.NCF4 −0.00249 −0.00298 3 5 Yes CLEC4E.AQP9.NCF4 −0.00016 −0.00042 3 5 Yes S100A12.CLEC4E.AQP9 −0.00103 −0.00073 3 5 Yes S100A12.CLEC4E.NCF4 0.000243 9.50E−05 3 5 Yes S100A8.AQP9.NCF4 −0.00108 −0.00207 3 5 Yes S100A12.S100A8.AQP9 −0.00226 −0.00336 3 5 Yes S100A12.S100A8.NCF4 −0.00141 −0.00262 3 5 Yes CLEC4E.S100A8.AQP9 3.17E−05 0.000338 3 5 Yes CLEC4E.S100A8.NCF4 0.000771 0.000813 3 5 Yes S100A12.AQP9 −0.0026 −0.00363 2 5 Yes S100A12.NCF4 −0.00227 −0.00284 2 5 Yes CLEC4E.AQP9 −0.00053 −0.0009 2 5 Yes CLEC4E.NCF4 −0.00024 −0.00076 2 5 Yes S100A8.AQP9 −0.00246 −0.00325 2 5 Yes S100A8.NCF4 −0.00091 −0.00302 2 5 Yes MMP9.ALOX5AP.NAMPT.GLT1D1.NCF2 −0.00325 −0.00535 5 5 Yes MMP9.ALOX5AP.GLT1D1.NCF2 −0.00498 −0.00693 4 5 No MMP9.NAMPT.GLT1D1.NCF2 −0.00311 −0.00518 4 5 Yes ALOX5AP.NAMPT.GLT1D1.NCF2 −0.00376 −0.0057 4 5 Yes MMP9.ALOX5AP.NAMPT.GLT1D1 −0.00339 −0.0054 4 5 Yes MMP9.ALOX5AP.NAMPT.NCF2 −0.00509 −0.00703 4 5 No MMP9.GLT1D1.NCF2 −0.00523 −0.0071 3 5 No ALOX5AP.GLT1D1.NCF2 −0.00402 −0.00594 3 5 Yes MMP9.ALOX5AP.GLT1D1 −0.00344 −0.00538 3 5 Yes MMP9.ALOX5AP.NCF2 −0.00488 −0.00691 3 5 No NAMPT.GLT1D1.NCF2 −0.00296 −0.00516 3 5 Yes MMP9.NAMPT.GLT1D1 −0.0033 −0.00529 3 5 Yes MMP9.NAMPT.NCF2 −0.00537 −0.00736 3 5 No ALOX5AP.NAMPT.GLT1D1 −0.00362 −0.00534 3 5 Yes ALOX5AP.NAMPT.NCF2 −0.0036 −0.0056 3 5 Yes MMP9.GLT1D1 −0.00518 −0.00711 2 5 No MMP9.NCF2 −0.00516 −0.00706 2 5 No ALOX5AP.GLT1D1 −0.00404 −0.00613 2 5 Yes ALOX5AP.NCF2 −0.00433 −0.00623 2 5 No NAMPT.GLT1D1 −0.00266 −0.00437 2 5 Yes NAMPT.NCF2 −0.00303 −0.00566 2 5 Yes S100A12.AQP9 −0.0026 −0.00363 2 5 Yes S100A12.NCF4 −0.00227 −0.00284 2 5 Yes S100A12.GLT1D1 −0.00245 −0.00356 2 5 Yes S100A12.NCF2 −0.00359 −0.00476 2 5 Yes CLEC4E.AQP9 −0.00053 −0.0009 2 5 Yes CLEC4E.NCF4 −0.00024 −0.00076 2 5 Yes CLEC4E.GLT1D1 −0.00023 −0.001 2 5 Yes CLEC4E.NCF2 −0.00113 −0.00249 2 5 Yes S100A8.AQP9 −0.00246 −0.00325 2 5 Yes S100A8.NCF4 −0.00091 −0.00302 2 5 Yes S100A8.GLT1D1 −0.00209 −0.00289 2 5 Yes S100A8.NCF2 −0.00297 −0.00497 2 5 Yes MMP9.AQP9 −0.00341 −0.00537 2 5 Yes MMP9.NCF4 −0.00317 −0.00544 2 5 Yes MMP9.GLT1D1 −0.00518 −0.00711 2 5 No MMP9.NCF2 −0.00516 −0.00706 2 5 No ALOX5AP.AQP9 −0.00481 −0.00669 2 5 No ALOX5AP.NCF4 −0.00386 −0.00645 2 5 Mixed ALOX5AP.GLT1D1 −0.00404 −0.00613 2 5 Yes ALOX5AP.NCF2 −0.00433 −0.00623 2 5 No NAMPT.AQP9 −0.00221 −0.00344 2 5 Yes NAMPT.NCF4 −0.00186 −0.00385 2 5 Yes NAMPT.GLT1D1 −0.00266 −0.00437 2 5 Yes NAMPT.NCF2 −0.00303 −0.00566 2 5 Yes

TABLE 22 Delta AUC Delta AUC Ridge Logistic Markers Regression Regression Markers Term Predictive CASP5.IL18RAP.TNFAIP6.IL8RB.TNFRSF10C.KCNE3.TLR4 0 0 7 6 Yes CASP5.IL18RAP.IL8RB.TNFRSF10C.KCNE3.TLR4 −0.00726 −0.00732 6 6 Yes CASP5.TNFAIP6.IL8RB.TNFRSF10C.KCNE3.TLR4 −0.00772 −0.00773 6 6 Yes IL18RAP.TNFAIP6.IL8RB.TNFRSF10C.KCNE3.TLR4 0.00226 0.002999 6 6 Yes CASP5.IL18RAP.TNFAIP6.IL8RB.TNFRSF10C.KCNE3 −0.00147 −0.00101 6 6 Yes CASP5.IL18RAP.TNFAIP6.IL8RB.TNFRSF10C.TLR4 0.001077 0.001045 6 6 Yes CASP5.IL18RAP.TNFAIP6.IL8RB.KCNE3.TLR4 0.000644 0.000296 6 6 Yes CASP5.IL18RAP.TNFAIP6.TNFRSF10C.KCNE3.TLR4 −0.0012 −0.00038 6 6 Yes CASP5.IL8RB.TNFRSF10C.KCNE3.TLR4 −0.01475 −0.01514 5 6 Yes IL18RAP.IL8RB.TNFRSF10C.KCNE3.TLR4 −0.00966 −0.00894 5 6 Yes CASP5.IL18RAP.IL8RB.TNFRSF10C.KCNE3 −0.00788 −0.008 5 6 Yes CASP5.IL18RAP.IL8RB.TNFRSF10C.TLR4 −0.00707 −0.00668 5 6 Yes CASP5.IL18RAP.IL8RB.KCNE3.TLR4 −0.00706 −0.00693 5 6 Yes CASP5.IL18RAP.TNFRSF10C.KCNE3.TLR4 −0.00785 −0.00776 5 6 Yes TNFAIP6.IL8RB.TNFRSF10C.KCNE3.TLR4 −0.00696 −0.00669 5 6 Yes CASP5.TNFAIP6.IL8RB.TNFRSF10C.KCNE3 −0.00842 −0.0081 5 6 Yes CASP5.TNFAIP6.IL8RB.TNFRSF10C.TLR4 −0.00771 −0.00776 5 6 Yes CASP5.TNFAIP6.IL8RB.KCNE3.TLR4 −0.00722 −0.00741 5 6 Yes CASP5.TNFAIP6.TNFRSF10C.KCNE3.TLR4 −0.00815 −0.00809 5 6 Yes IL18RAP.TNFAIP6.IL8RB.TNFRSF10C.KCNE3 0.001066 0.00151 5 6 Yes IL18RAP.TNFAIP6.IL8RB.TNFRSF10C.TLR4 0.001795 0.003252 5 6 Yes IL18RAP.TNFAIP6.IL8RB.KCNE3.TLR4 0.002745 0.003822 5 6 Yes IL18RAP.TNFAIP6.TNFRSF10C.KCNE3.TLR4 0.001626 0.003305 5 6 Yes CASP5.IL18RAP.TNFAIP6.IL8RB.TNFRSF10C −0.00061 −0.00029 5 6 Yes CASP5.IL18RAP.TNFAIP6.IL8RB.KCNE3 −0.00103 1.06E−05 5 6 Yes CASP5.IL18RAP.TNFAIP6.TNFRSF10C.KCNE3 −0.00291 −0.00322 5 6 Yes CASP5.IL18RAP.TNFAIP6.IL8RB.TLR4 0.002492 0.002714 5 6 Yes CASP5.IL18RAP.TNFAIP6.TNFRSF10C.TLR4 −0.0005 −0.00052 5 6 Yes CASP5.IL18RAP.TNFAIP6.KCNE3.TLR4 −0.00096 −0.0004 5 6 Yes CASP5.IL8RB.TNFRSF10C.KCNE3 −0.01477 −0.01564 4 6 Yes CASP5.IL8RB.TNFRSF10C.TLR4 −0.01456 −0.01506 4 6 Yes CASP5.IL8RB.KCNE3.TLR4 −0.01438 −0.01466 4 6 Yes CASP5.TNFRSF10C.KCNE3.TLR4 −0.01449 −0.01482 4 6 Yes IL18RAP.IL8RB.TNFRSF10C.KCNE3 −0.00979 −0.00959 4 6 Yes IL18RAP.IL8RB.TNFRSF10C.TLR4 −0.00982 −0.00881 4 6 Yes IL18RAP.IL8RB.KCNE3.TLR4 −0.00926 −0.00856 4 6 Yes IL18RAP.TNFRSF10C.KCNE3.TLR4 −0.00952 −0.00899 4 6 Yes CASP5.IL18RAP.IL8RB.TNFRSF10C −0.00777 −0.00745 4 6 Yes CASP5.IL18RAP.IL8RB.KCNE3 −0.00763 −0.00746 4 6 Yes CASP5.IL18RAP.TNFRSF10C.KCNE3 −0.00871 −0.00871 4 6 Yes CASP5.IL18RAP.IL8RB.TLR4 −0.00598 −0.00571 4 6 Yes CASP5.IL18RAP.TNFRSF10C.TLR4 −0.00733 −0.00724 4 6 Yes CASP5.IL18RAP.KCNE3.TLR4 −0.00755 −0.00775 4 6 Yes TNFAIP6.IL8RB.TNFRSF10C.KCNE3 −0.00715 −0.00729 4 6 Yes TNFAIP6.IL8RB.TNFRSF10C.TLR4 −0.00744 −0.00638 4 6 Yes TNFAIP6.IL8RB.KCNE3.TLR4 −0.00641 −0.00627 4 6 Yes TNFAIP6.TNFRSF10C.KCNE3.TLR4 −0.00668 −0.00641 4 6 Yes CASP5.TNFAIP6.IL8RB.TNFRSF10C −0.00867 −0.0087 4 6 Yes CASP5.TNFAIP6.IL8RB.KCNE3 −0.00781 −0.00814 4 6 Yes CASP5.TNFAIP6.TNFRSF10C.KCNE3 −0.00926 −0.00908 4 6 Yes CASP5.TNFAIP6.IL8RB.TLR4 −0.0068 −0.00666 4 6 Yes CASP5.TNFAIP6.TNFRSF10C.TLR4 −0.00852 −0.00834 4 6 Yes CASP5.TNFAIP6.KCNE3.TLR4 −0.00793 −0.00791 4 6 Yes IL18RAP.TNFAIP6.IL8RB.TNFRSF10C −6.34E−05 0.000813 4 6 Yes IL18RAP.TNFAIP6.IL8RB.KCNE3 0.001542 0.002175 4 6 Yes IL18RAP.TNFAIP6.TNFRSF10C.KCNE3 0.000285 0.00057 4 6 Yes IL18RAP.TNFAIP6.IL8RB.TLR4 0.003347 0.004878 4 6 Yes IL18RAP.TNFAIP6.TNFRSF10C.TLR4 0.001594 0.002502 4 6 Yes IL18RAP.TNFAIP6.KCNE3.TLR4 0.001795 0.002566 4 6 Yes CASP5.IL18RAP.TNFAIP6.IL8RB −0.00015 0.001193 4 6 Yes CASP5.IL18RAP.TNFAIP6.TNFRSF10C −0.00385 −0.00361 4 6 Yes CASP5.IL18RAP.TNFAIP6.KCNE3 −0.00364 −0.0031 4 6 Yes CASP5.IL18RAP.TNFAIP6.TLR4 −0.00058 −0.00041 4 6 Yes CASP5.IL8RB.TNFRSF10C −0.01523 −0.01583 3 6 Yes CASP5.IL8RB.KCNE3 −0.01468 −0.01507 3 6 Yes CASP5.TNFRSF10C.KCNE3 −0.01494 −0.01568 3 6 Yes CASP5.IL8RB.TLR4 −0.01426 −0.01487 3 6 Yes CASP5.TNFRSF10C.TLR4 −0.01482 −0.0152 3 6 Yes CASP5.KCNE3.TLR4 −0.01459 −0.01474 3 6 Yes IL18RAP.IL8RB.TNFRSF10C −0.01037 −0.00964 3 6 Yes IL18RAP.IL8RB.KCNE3 −0.01 −0.00946 3 6 Yes IL18RAP.TNFRSF10C.KCNE3 −0.01029 −0.00963 3 6 Yes IL18RAP.IL8RB.TLR4 −0.00926 −0.00805 3 6 Yes IL18RAP.TNFRSF10C.TLR4 −0.00991 −0.0089 3 6 Yes IL18RAP.KCNE3.TLR4 −0.0091 −0.00833 3 6 Yes CASP5.IL18RAP.IL8RB −0.00718 −0.00648 3 6 Yes CASP5.IL18RAP.TNFRSF10C −0.00915 −0.00916 3 6 Yes CASP5.IL18RAP.KCNE3 −0.00891 −0.00872 3 6 Yes CASP5.IL18RAP.TLR4 −0.00688 −0.00775 3 6 Yes TNFAIP6.IL8RB.TNFRSF10C −0.00786 −0.00752 3 6 Yes TNFAIP6.IL8RB.KCNE3 −0.00686 −0.00644 3 6 Yes TNFAIP6.TNFRSF10C.KCNE3 −0.00771 −0.00749 3 6 Yes TNFAIP6.IL8RB.TLR4 −0.00668 −0.0056 3 6 Yes TNFAIP6.TNFRSF10C.TLR4 −0.00765 −0.0068 3 6 Yes TNFAIP6.KCNE3.TLR4 −0.00623 −0.00608 3 6 Yes CASP5.TNFAIP6.IL8RB −0.0079 −0.00708 3 6 Yes CASP5.TNFAIP6.TNFRSF10C −0.00996 −0.00988 3 6 Yes CASP5.TNFAIP6.KCNE3 −0.00935 −0.00929 3 6 Yes CASP5.TNFAIP6.TLR4 −0.00745 −0.00787 3 6 Yes IL18RAP.TNFAIP6.IL8RB 0.000549 0.00189 3 6 Yes IL18RAP.TNFAIP6.TNFRSF10C −0.00174 −0.00122 3 6 Yes IL18RAP.TNFAIP6.KCNE3 −0.00038 0.00037 3 6 Yes IL18RAP.TNFAIP6.TLR4 0.001552 0.002249 3 6 Yes CASP5.IL8RB −0.01498 −0.01537 2 6 Yes CASP5.TNFRSF10C −0.01527 −0.01602 2 6 Yes CASP5.KCNE3 −0.01471 −0.0152 2 6 Yes CASP5.TLR4 −0.01426 −0.01449 2 6 Yes IL18RAP.IL8RB −0.00983 −0.00922 2 6 Yes IL18RAP.TNFRSF10C −0.01126 −0.01029 2 6 Yes IL18RAP.KCNE3 −0.0097 −0.01001 2 6 Yes IL18RAP.TLR4 −0.00878 −0.00829 2 6 Yes TNFAIP6.IL8RB −0.00724 −0.00667 2 6 Yes TNFAIP6.TNFRSF10C −0.00924 −0.00868 2 6 Yes TNFAIP6.KCNE3 −0.00752 −0.00694 2 6 Yes TNFAIP6.TLR4 −0.00663 −0.00632 2 6 Yes H3F3B.TXN.PLAUR.BCL2A1.PTAFR.LAMP2.TYROBP −0.01883 −0.01929 7 6 Yes H3F3B.TXN.BCL2A1.PTAFR.LAMP2.TYROBP −0.01979 −0.02006 6 6 Yes H3F3B.PLAUR.BCL2A1.PTAFR.LAMP2.TYROBP −0.01852 −0.01892 6 6 Yes TXN.PLAUR.BCL2A1.PTAFR.LAMP2.TYROBP −0.01845 −0.01856 6 6 Yes H3F3B.TXN.PLAUR.BCL2A1.PTAFR.LAMP2 −0.01891 −0.01913 6 6 Yes H3F3B.TXN.PLAUR.BCL2A1.PTAFR.TYROBP −0.01909 −0.01945 6 6 Yes H3F3B.TXN.PLAUR.BCL2A1.LAMP2.TYROBP −0.01989 −0.02008 6 6 Yes H3F3B.TXN.PLAUR.PTAFR.LAMP2.TYROBP −0.01823 −0.01798 6 6 Yes H3F3B.BCL2A1.PTAFR.LAMP2.TYROBP −0.01985 −0.01997 5 6 Yes TXN.BCL2A1.PTAFR.LAMP2.TYROBP −0.01978 −0.0199 5 6 Yes H3F3B.TXN.BCL2A1.PTAFR.LAMP2 −0.0195 −0.01968 5 6 Yes H3F3B.TXN.BCL2A1.PTAFR.TYROBP −0.02001 −0.02022 5 6 Yes H3F3B.TXN.BCL2A1.LAMP2.TYROBP −0.02077 −0.02088 5 6 No H3F3B.TXN.PTAFR.LAMP2.TYROBP −0.01929 −0.01949 5 6 Yes PLAUR.BCL2A1.PTAFR.LAMP2.TYROBP −0.0192 −0.0192 5 6 Yes H3F3B.PLAUR.BCL2A1.PTAFR.LAMP2 −0.01879 −0.01932 5 6 Yes H3F3B.PLAUR.BCL2A1.PTAFR.TYROBP −0.01904 −0.0194 5 6 Yes H3F3B.PLAUR.BCL2A1.LAMP2.TYROBP −0.01934 −0.01953 5 6 Yes H3F3B.PLAUR.PTAFR.LAMP2.TYROBP −0.01801 −0.01798 5 6 Yes TXN.PLAUR.BCL2A1.PTAFR.LAMP2 −0.01863 −0.01884 5 6 Yes TXN.PLAUR.BCL2A1.PTAFR.TYROBP −0.01971 −0.01955 5 6 Yes TXN.PLAUR.BCL2A1.LAMP2.TYROBP −0.01923 −0.01932 5 6 Yes TXN.PLAUR.PTAFR.LAMP2.TYROBP −0.01834 −0.01792 5 6 Yes H3F3B.TXN.PLAUR.BCL2A1.PTAFR −0.01945 −0.01958 5 6 Yes H3F3B.TXN.PLAUR.BCL2A1.LAMP2 −0.01999 −0.02012 5 6 Yes H3F3B.TXN.PLAUR.PTAFR.LAMP2 −0.01816 −0.01784 5 6 Yes H3F3B.TXN.PLAUR.BCL2A1.TYROBP −0.02038 −0.02064 5 6 Yes H3F3B.TXN.PLAUR.PTAFR.TYROBP −0.01949 −0.0195 5 6 Yes H3F3B.TXN.PLAUR.LAMP2.TYROBP −0.01904 −0.01909 5 6 Yes H3F3B.BCL2A1.PTAFR.LAMP2 −0.01974 −0.02007 4 6 Yes H3F3B.BCL2A1.PTAFR.TYROBP −0.01984 −0.02014 4 6 Yes H3F3B.BCL2A1.LAMP2.TYROBP −0.01983 −0.02014 4 6 Yes H3F3B.PTAFR.LAMP2.TYROBP −0.01896 −0.01932 4 6 Yes TXN.BCL2A1.PTAFR.LAMP2 −0.01948 −0.01943 4 6 Yes TXN.BCL2A1.PTAFR.TYROBP −0.02009 −0.02005 4 6 Yes TXN.BCL2A1.LAMP2.TYROBP −0.02014 −0.02012 4 6 Yes TXN.PTAFR.LAMP2.TYROBP −0.01931 −0.01992 4 6 Yes H3F3B.TXN.BCL2A1.PTAFR −0.02017 −0.02014 4 6 Yes H3F3B.TXN.BCL2A1.LAMP2 −0.02053 −0.02056 4 6 No H3F3B.TXN.PTAFR.LAMP2 −0.01819 −0.01867 4 6 Yes H3F3B.TXN.BCL2A1.TYROBP −0.02078 −0.02122 4 6 No H3F3B.TXN.PTAFR.TYROBP −0.02018 −0.02033 4 6 Yes H3F3B.TXN.LAMP2.TYROBP −0.01999 −0.02022 4 6 Yes PLAUR.BCL2A1.PTAFR.LAMP2 −0.0192 −0.01915 4 6 Yes PLAUR.BCL2A1.PTAFR.TYROBP −0.01966 −0.02001 4 6 Yes PLAUR.BCL2A1.LAMP2.TYROBP −0.01947 −0.01983 4 6 Yes PLAUR.PTAFR.LAMP2.TYROBP −0.01847 −0.01839 4 6 Yes H3F3B.PLAUR.BCL2A1.PTAFR −0.01925 −0.01924 4 6 Yes H3F3B.PLAUR.BCL2A1.LAMP2 −0.01962 −0.01999 4 6 Yes H3F3B.PLAUR.PTAFR.LAMP2 −0.01779 −0.01779 4 6 Yes H3F3B.PLAUR.BCL2A1.TYROBP −0.01978 −0.02003 4 6 Yes H3F3B.PLAUR.PTAFR.TYROBP −0.01903 −0.01932 4 6 Yes H3F3B.PLAUR.LAMP2.TYROBP −0.01835 −0.01842 4 6 Yes TXN.PLAUR.BCL2A1.PTAFR −0.01895 −0.0189 4 6 Yes TXN.PLAUR.BCL2A1.LAMP2 −0.01947 −0.01941 4 6 Yes TXN.PLAUR.PTAFR.LAMP2 −0.01748 −0.01746 4 6 Yes TXN.PLAUR.BCL2A1.TYROBP −0.02086 −0.02136 4 6 No TXN.PLAUR.PTAFR.TYROBP −0.01936 −0.01917 4 6 Yes TXN.PLAUR.LAMP2.TYROBP −0.01876 −0.01874 4 6 Yes H3F3B.TXN.PLAUR.BCL2A1 −0.02057 −0.02098 4 6 No H3F3B.TXN.PLAUR.PTAFR −0.0182 −0.01799 4 6 Yes H3F3B.TXN.PLAUR.LAMP2 −0.0185 −0.0186 4 6 Yes H3F3B.TXN.PLAUR.TYROBP −0.02026 −0.02086 4 6 Yes H3F3B.BCL2A1.PTAFR −0.0199 −0.02011 3 6 Yes H3F3B.BCL2A1.LAMP2 −0.02034 −0.02054 3 6 Yes H3F3B.PTAFR.LAMP2 −0.01899 −0.01887 3 6 Yes H3F3B.BCL2A1.TYROBP −0.02078 −0.02105 3 6 No H3F3B.PTAFR.TYROBP −0.02001 −0.02029 3 6 Yes H3F3B.LAMP2.TYROBP −0.01974 −0.02007 3 6 Yes TXN.BCL2A1.PTAFR −0.01986 −0.01992 3 6 Yes TXN.BCL2A1.LAMP2 −0.01978 −0.02016 3 6 Yes TXN.PTAFR.LAMP2 −0.01891 −0.01887 3 6 Yes TXN.BCL2A1.TYROBP −0.02107 −0.02132 3 6 No TXN.PTAFR.TYROBP −0.02 −0.02024 3 6 Yes TXN.LAMP2.TYROBP −0.01962 −0.02003 3 6 Yes H3F3B.TXN.BCL2A1 −0.0211 −0.02123 3 6 No H3F3B.TXN.PTAFR −0.01906 −0.01911 3 6 Yes H3F3B.TXN.LAMP2 −0.0193 −0.01958 3 6 Yes H3F3B.TXN.TYROBP −0.0212 −0.02144 3 6 No PLAUR.BCL2A1.PTAFR −0.01948 −0.01956 3 6 Yes PLAUR.BCL2A1.LAMP2 −0.01952 −0.0198 3 6 Yes PLAUR.PTAFR.LAMP2 −0.01797 −0.01784 3 6 Yes PLAUR.BCL2A1.TYROBP −0.02012 −0.02041 3 6 Yes PLAUR.PTAFR.TYROBP −0.01907 −0.01933 3 6 Yes PLAUR.LAMP2.TYROBP −0.01889 −0.01879 3 6 Yes H3F3B.PLAUR.BCL2A1 −0.02017 −0.02029 3 6 Yes H3F3B.PLAUR.PTAFR −0.01819 −0.01819 3 6 Yes H3F3B.PLAUR.LAMP2 −0.01851 −0.01851 3 6 Yes H3F3B.PLAUR.TYROBP −0.01981 −0.01982 3 6 Yes TXN.PLAUR.BCL2A1 −0.02091 −0.02139 3 6 No TXN.PLAUR.PTAFR −0.01811 −0.01817 3 6 Yes TXN.PLAUR.LAMP2 −0.01777 −0.01807 3 6 Yes TXN.PLAUR.TYROBP −0.02058 −0.02118 3 6 No H3F3B.BCL2A1 −0.02045 −0.02078 2 6 No H3F3B.PTAFR −0.01928 −0.0197 2 6 Yes H3F3B.LAMP2 −0.01952 −0.01982 2 6 Yes H3F3B.TYROBP −0.02049 −0.02082 2 6 No TXN.BCL2A1 −0.02134 −0.02159 2 6 No TXN.PTAFR −0.01891 −0.01933 2 6 Yes TXN.LAMP2 −0.01943 −0.0199 2 6 Yes TXN.TYROBP −0.02144 −0.02172 2 6 No PLAUR.BCL2A1 −0.0202 −0.02062 2 6 Yes PLAUR.PTAFR −0.01811 −0.01813 2 6 Yes PLAUR.LAMP2 −0.01833 −0.01797 2 6 Yes PLAUR.TYROBP −0.01995 −0.02038 2 6 Yes CASP5.IL8RB1 −0.01498 −0.01537 2 6 Yes CASP5.TNFRSF10C1 −0.01527 −0.01602 2 6 Yes CASP5.KCNE31 −0.01471 −0.0152 2 6 Yes CASP5.TLR41 −0.01426 −0.01449 2 6 Yes CASP5.BCL2A1 −0.01551 −0.016 2 6 Yes CASP5.PTAFR −0.01515 −0.01538 2 6 Yes CASP5.LAMP2 −0.0153 −0.01552 2 6 Yes CASP5.TYROBP −0.01642 −0.01626 2 6 Yes IL18RAP.IL8RB1 −0.00983 −0.00922 2 6 Yes IL18RAP.TNFRSF10C1 −0.01126 −0.01029 2 6 Yes IL18RAP.KCNE31 −0.0097 −0.01001 2 6 Yes IL18RAP.TLR41 −0.00878 −0.00829 2 6 Yes IL18RAP.BCL2A1 −0.01217 −0.01143 2 6 Yes IL18RAP.PTAFR −0.01153 −0.01101 2 6 Yes IL18RAP.LAMP2 −0.01012 −0.00998 2 6 Yes IL18RAP.TYROBP −0.01198 −0.01196 2 6 Yes TNFAIP6.IL8RB1 −0.00724 −0.00667 2 6 Yes TNFAIP6.TNFRSF10C1 −0.00924 −0.00868 2 6 Yes TNFAIP6.KCNE31 −0.00752 −0.00694 2 6 Yes TNFAIP6.TLR41 −0.00663 −0.00632 2 6 Yes TNFAIP6.BCL2A1 −0.0102 −0.0097 2 6 Yes TNFAIP6.PTAFR −0.00952 −0.00906 2 6 Yes TNFAIP6.LAMP2 −0.00845 −0.00774 2 6 Yes TNFAIP6.TYROBP −0.01093 −0.0105 2 6 Yes H3F3B.IL8RB −0.0199 −0.02009 2 6 Yes H3F3B.TNFRSF10C −0.01956 −0.01973 2 6 Yes H3F3B.KCNE3 −0.01699 −0.0169 2 6 Yes H3F3B.TLR4 −0.01707 −0.01722 2 6 Yes H3F3B.BCL2A1 −0.02045 −0.02078 2 6 No H3F3B.PTAFR −0.01928 −0.0197 2 6 Yes H3F3B.LAMP2 −0.01952 −0.01982 2 6 Yes H3F3B.TYROBP −0.02049 −0.02082 2 6 No TXN.IL8RB −0.02074 −0.02073 2 6 No TXN.TNFRSF10C −0.02084 −0.02143 2 6 No TXN.KCNE3 −0.01775 −0.01731 2 6 Yes TXN.TLR4 −0.01782 −0.01819 2 6 Yes TXN.BCL2A1 −0.02134 −0.02159 2 6 No TXN.PTAFR −0.01891 −0.01933 2 6 Yes TXN.LAMP2 −0.01943 −0.0199 2 6 Yes TXN.TYROBP −0.02144 −0.02172 2 6 No PLAUR.IL8RB −0.01926 −0.0191 2 6 Yes PLAUR.TNFRSF10C −0.01955 −0.01981 2 6 Yes PLAUR.KCNE3 −0.01482 −0.01437 2 6 Yes PLAUR.TLR4 −0.0171 −0.01682 2 6 Yes PLAUR.BCL2A1 −0.0202 −0.02062 2 6 Yes PLAUR.PTAFR −0.01811 −0.01813 2 6 Yes PLAUR.LAMP2 −0.01833 −0.01797 2 6 Yes PLAUR.TYROBP −0.01995 −0.02038 2 6 Yes

TABLE 23 Delta AUC Delta AUC Ridge Logistic Markers Regression Regression Markers Term Predictive CD3D.TMC8.SLAMF7.KLRC4 0 0 4 7 Yes CD3D.SLAMF7.KLRC4 −6.34E−05 1.06E−05 3 7 Yes TMC8.SLAMF7.KLRC4 −0.00094 −0.00103 3 7 Yes CD3D.TMC8.SLAMF7 0.003432 0.004065 3 7 Yes CD3D.TMC8.KLRC4 −0.00269 −0.00335 3 7 Yes CD3D.SLAMF7 0.00226 0.002766 2 7 Yes CD3D.KLRC4 −0.0027 −0.00332 2 7 Yes TMC8.SLAMF7 0.001594 0.001827 2 7 Yes TMC8.KLRC4 −0.00351 −0.00365 2 7 Yes LCK.CCT2.CX3CR1.CD8A −0.01192 −0.01099 4 7 No LCK.CX3CR1.CD8A −0.01192 −0.01143 3 7 No CCT2.CX3CR1.CD8A −0.01192 −0.01216 3 7 No LCK.CCT2.CX3CR1 −0.00644 −0.00646 3 7 Yes LCK.CCT2.CD8A −0.01323 −0.01304 3 7 No LCK.CX3CR1 −0.00687 −0.00729 2 7 Yes LCK.CD8A −0.01382 −0.01289 2 7 No CCT2.CX3CR1 −0.00646 −0.0061 2 7 Yes CCT2.CD8A −0.01287 −0.01253 2 7 No CD3D.SLAMF7 0.00226 0.002766 2 7 Yes CD3D.KLRC4 −0.0027 −0.00332 2 7 Yes CD3D.CX3CR1 −0.00589 −0.00615 2 7 Yes CD3D.CD8A −0.01374 −0.01231 2 7 No TMC8.SLAMF7 0.001594 0.001827 2 7 Yes TMC8.KLRC4 −0.00351 −0.00365 2 7 Yes TMC8.CX3CR1 −0.00572 −0.00602 2 7 Yes TMC8.CD8A −0.01199 −0.01121 2 7 No LCK.SLAMF7 0.000116 0.000285 2 7 Yes LCK.KLRC4 −0.00436 −0.0045 2 7 Yes LCK.CX3CR1 −0.00687 −0.00729 2 7 Yes LCK.CD8A −0.01382 −0.01289 2 7 No CCT2.SLAMF7 0.001795 0.002154 2 7 Yes CCT2.KLRC4 −0.00403 −0.00408 2 7 Yes CCT2.CX3CR1 −0.00646 −0.0061 2 7 Yes CCT2.CD8A −0.01287 −0.01253 2 7 No

TABLE 24 Clinical and Demographic Characteristics of the Final Development and Validation Patient Sets¹ Development Validation Obstructive CAD² No Obstructive CAD Obstructive CAD No Obstructive CAD (N = 230) (N = 410) P-value (N = 192) (N = 334) P-value Characteristic Age, mean (SD), y 63.7 (11.1) 57.2 (11.8) <0.001 64.7 (9.8) 57.7 (11.7) <0.001 Men, No. (%) 180 (78.3%) 193 (47.1%) <0.001 134 (69.8%) 165 (49.4%) <0.001 Chest pain type <0.001 <0.001 Typical 61 (26.5%) 66 (16.1%) 42 (21.9%) 41 (12.3%) Atypical 28 (12.2%) 56 (13.7%) 42 (21.9%) 49 (14.7%) Non-cardiac 47 (20.4%) 137 (33.4%) 50 (26.0%) 134 (40.1%) None 91 (39.6%) 143 (34.9%) 58 (30.2%) 109 (32.6%) Blood pressure, mean (SD), mm Hg Systolic 138 (17.7) 133 (18.3) <0.001 140 (17.7) 132 (18.1) <0.001 Diastolic 79.7 (11.0) 79.6 (11.7) 0.94 79.2 (11.3) 77.5 (10.9) 0.09 Hypertension 163 (70.9%) 237 (57.8%)   0.002 142 (74.0%) 203 (60.8%)   0.001 Dyslipidemia 170 (73.9%) 225 (54.9%) <0.001 133 (69.3%) 208 (62.3%) 0.11 Curent smoking 53 (23.2%) 99 (24.3%) 0.75 38 (19.8%) 68 (20.4%) 0.70 BMI, mean (SD), kg/m2 30.5 (6.0) 31.0 (7.5) 0.35 29.8 (5.5) 31.3 (7.0) 0.01 Ethnicity, White not Hispanic 210 (91.3%) 347 (84.6%)   0.016 181 (94.3%) 293 (87.7%) 0.02 Clinical syndrome Stable angina 123 (53.5%) 214 (52.2%) 0.78 107 (55.7%) 176 (52.7%) 0.46 Unstable angina 35 (15.2%) 81 (19.8%) 0.15 31 (16.1%) 58 (17.4%) 0.74 Asymptomatic, high risk 72 (31.3%) 113 (27.6%) 0.32 53 (27.6%) 100 (29.9%) 0.60 Medications Aspirin and salicylates 153 (66.5%) 232 (56.6%) 0.03 139 (72.4%) 205 (61.4%) 0.01 Statins 109 (47.4%) 142 (34.6%)   0.003 93 (48.4%) 127 (38.0%) 0.02 Beta blockers 82 (35.7%) 133 (32.4%) 0.52 85 (44.3%) 124 (37.1%) 0.11 ACE inhibitors 57 (24.8%) 67 (16.3%) 0.01 47 (24.5%) 64 (19.2%) 0.16 Angiotensin receptor 29 (12.6%) 39 (9.5%) 0.26 18 (9.4%) 34 (10.2%) 0.76 blockers Calcium channel blockers 33 (14.3%) 46 (11.2%) 0.29 26 (13.5%) 34 (10.2%) 0.25 Antiplatelet agents 27 (11.7%) 21 (5.1%)  0.003 16 (8.3%) 17 (5.1%) 0.14 Steroids, not systemic 23 (10.0%) 33 (8.0%) 0.45 19 (9.9%) 38 (11.4%) 0.59 NSAIDS 47 (20.4%) 78 (19.0%) 0.76 30 (15.6%) 58 (17.4%) 0.60 ¹Characteristics of the 640 subjects in the Algorithm Development and 526 subjects in the Validation sets. P values were calculated by t-tests for continuous variables and using chi-square tests for discrete variables. Significant p values in both sets are bolded and underlined and are bolded if significant in single sets. ²Obstructive CAD is defined as >50% luminal stenosis in ≧1 major vessel by QCA

TABLE 25A Reclassification analysis of Gene Expression Algorithm with Diamond-Forrester Clinical Model With Gene Expression Algorithm Reclassified % Low Int. High Total Lower Higher Total D-F Low Risk Patients 118 96 38 252 0.0 15.1 15.1 included Disease pts 16 19 22 57 0.0 38.6 38.6 Non disease pts 102 77 16 195 0.0 8.2 8.2 Observed risk 14% 20% 58% 23% — — — D-F Int Risk Patients 28 21 47 96 29.2 49.0 78.1 included Disease pts 7 11 26 44 15.9 59.1 75.0 Non disease pts 21 10 21 52 40.4 40.4 80.8 Observed risk 25% 52% 55% 46% — — — D-F High Risk Patients 28 60 89 177 15.8 0.0 15.8 included Disease pts 6 29 56 91 6.6 0.0 6.6 Non disease pts 22 31 33 86 38.4 0.0 38.4 Observed risk 21% 48% 63% 51% — — — Total Patients 174 77 174 525 included Disease pts 29 59 104 192 Non disease pts 145 118 70 333 Observed risk 17% 33% 60% 37% Risk categories:: Low = 0-<20%, Intermediate = ≧20-50%, High = ≧50%. Classification improved in 18.2% of disease patients and improved in 1.8% of non disease patients for a net reclassification improvement of 20.0% (p < .001)

TABLE 25B Reclassification analysis of Gene Expression Algorithm with MPI Results With Gene Expression Algorithm Reclassified % Low Int. High Total Lower Higher Total MPI Negative Patients 41 31 15 87 0.0 17.4 17.4 included Disease pts 7 8 7 22 0.0 31.8 31.8 Non disease pts 34 23 8 65 0.0 12.3 12.3 Observed risk 17% 26% 47% 25% — — — MPI Positive Patients 57 78 88 223 25.6 0.0 25.6 included Disease pts 6 21 49 76 7.9 0.0 7.9 Non disease pts 51 57 39 147 34.7 0.0 34.7 Observed risk 11% 27% 56% 34% — — — Total Patients 98 109 103 310 included Disease pts 13 29 56 98 Non disease pts 85 80 47 212 Observed risk 13% 27% 54% 32% Risk categories:: Low = 0-<20%, Intermediate = ≧20-50%, High = ≧50%. Classification improved in 1.0% of disease patients and improved in 20.3% of non disease patients for a net reclassification improvement of 21.3% (p < .001)

SEQUENCE LISTING

Primers and Probes Forward Reverse Assay ID Symbol Primer Primer Probe CDXR0728-SP1 AF289562 ACAGGAGGGAGGGATG GCCAATCACCTGCCTA TCAGGCAGCCCCCCAG CA ATGC AG (SEQ. ID NO. 1) (SEQ. ID NO. 2) (SEQ. ID NO. 3) CDXR0868-SP1 AQP9 ACCTGAGTCCCAGACT CCACTACAGGAATCCA CTTCAGAGCTGGAAAC TTTCACT CCAGAAG AA (SEQ. ID NO. 4) (SEQ. ID NO. 5) (SEQ. ID NO. 6) CDXR0830-SP1 CASP5 CGAGCAACCTTGACAA GGTAAATGTGCTCTTT CCTGTGGTTTCATTTT GAGATTTC GATGTTGACA C (SEQ. ID NO. 7) (SEQ. ID NO. 8) (SEQ. ID NO. 9) CDXR0884-SP2 CD79B CAGACGCTGCTGATCA TCGTAGGTGTGATCTT CCTTGCTGTCATCCTT TCCT CCTCCAT GTC (SEQ. ID NO. 10) (SEQ. ID NO. 11) (SEQ. ID NO. 12) CDXR0863-SP1 CLEC4E GGACGGCACACCTTTG CCTCCAGGGTAGCTAT CCCAGAAGCTCAGAGA ACA GTTGTTG CT (SEQ. ID NO. 13) (SEQ. ID NO. 14) (SEQ. ID NO. 15) CDXR0080-SP1 IL18RAP AGCCTGTGTTTGCTTG TCTTCTGCTTCTCTTA TCTTCTGCATACACTC AAAGAGAT ATAATGCTCACAA CTCC (SEQ. ID NO. 16) (SEQ. ID NO. 17) (SEQ. ID NO. 18) CDXR0832-SP1 IL8RB CCCCATTGTGGTCACA CCAGGGCAAGCTTTCT ACGTTCTTACTAGTTT GGAA AAACCAT CCC (SEQ. ID NO. 19) (SEQ. ID NO. 20) (SEQ. ID NO. 21) CDXR0888-SP0 KCNE3 TCTCTAAGGCTCTATC GCTGGAACCATATATG CCTACAAACACAGTGA AGTTCTGACAT AAACTACGATACT TTACA (SEQ. ID NO. 22) (SEQ. ID NO. 23) (SEQ. ID NO. 24) CDXR0861-SP1 KLRC4 TGTATTGGAGTACTGG CTGTTGGAATATGTAA CAATGACGTGCTTTCT AGCAGAACA TCCACTCCTCA G (SEQ. ID NO. 25) (SEQ. ID NO. 26) (SEQ. ID NO. 27) CDXR0826-SP1 NCF4 CTCCCAGAAGCGCCTC GGGACACCGTCAGCTC CACGCAGAAGGACAAC TT ATG T (SEQ. ID NO. 28) (SEQ. ID NO. 29) (SEQ. ID NO. 30) CDXR0056P1- S100A12 TCTCTAAGGGTGAGCT CCAGGCCTTGGAATAT CAAACACCATCAAGAA SP1 GAAGCA TTCATCAATG TAT (SEQ. ID NO. 31) (SEQ. ID NO. 32) (SEQ. ID NO. 33) CDXR0069P1- S100A8 GAAGAAATTGCTAGAG GCACCATCAGTGTTGA CACCCTTTTTCCTGAT SP1 ACCGAGTGT TATCCAACT ATACT (SEQ. ID NO. 34) (SEQ. ID NO. 35) (SEQ. ID NO. 36) CDXR0663-SP1 SLAMF7 AGCAAATACGGTTTAC GGCATCGTGAGCAGTG TTTTCCATCTTTTTCG TCCACTGT AGT GTATTTC (SEQ. ID NO. 37) (SEQ. ID NO. 38) (SEQ. ID NO. 39) CDXR0840-SP1 SPIB GAGGCCCTCGTGGCT TGGTACAGGCGCAGCT CTTGCGAGTCCCTGCC (SEQ. ID NO. 40) T TC (SEQ. ID NO. 41) (SEQ. ID NO. 42) CDXR0672-SP1 TFCP2 ACAGAACTTTCAGGAA CCGCACTCCTACTTCA ACAATGAAAGCAGAAA GAAGCATGT GTATGAT CC (SEQ. ID NO. 43) (SEQ. ID NO. 44) (SEQ. ID NO. 45) CDXR0891-SP0 TLR4 GGGAAGAGTGGATGTT GGATGAACATTCTTTT ATGTGTCTGGAATTAA ATCATTGAGAA CTGGGAACCT TG (SEQ. ID NO. 46) (SEQ. ID NO. 47) (SEQ. ID NO. 48) CDXR0876-SP1 TMC8 CACAGGCTCCGGAAG CGCGACAGGTCCTCCA CTGGTGTGGCAGGTTC CA C (SEQ. ID NO. 51) (SEQ. ID NO. 49) (SEQ. ID NO. 50) CDXR0857-SP1 TNFAIP6 GGAGATGAGCTTCCAG AGCTGTCACTGAAGCA CATCAGTACAGGAAAT ATGACAT TCACTTAG GTC (SEQ. ID NO. 52) (SEQ. ID NO. 53) (SEQ. ID NO. 54) CDXR0844-SP1 TNFRSF10C GGAATGAAAACTCCCC CAGGACGTACAATTAC CTAGGGCACCTGCTAC AGAGATGTG TGACTTGGA AC (SEQ. ID NO. 55) (SEQ. ID NO. 56) (SEQ. ID NO. 57) CDXR0121-SP1 AF161365 GCCTTGGAACACACCT CAGGACACACTTCCGA CCCCAGGAGTTGCTG TCGT TGGATTTA (SEQ. ID NO. 60) (SEQ. ID NO. 58) (SEQ. ID NO. 59) CDXR0703-SP1 HNRPF CCAGAAGTGTCTCCCA GGTGATCTTGGGTGTG TTTGTGGCTTAAAAAC CTGAAG GCTTT AACC (SEQ. ID NO. 61) (SEQ. ID NO. 62) (SEQ. ID NO. 63) A23P208358- RPL28 CGGACCACCATCAACA TTCTTGCGGATCATGT CTCGCGCCACGCTCA 188 AGAATG GTCTGA (SEQ. ID NO. 66) (SEQ. ID NO. 64) (SEQ. ID NO. 65) 

1. A method for determining coronary artery disease risk in a subject, comprising: performing at least one nucleotide-based assay on a sample from the subject to generate a dataset comprising data representing mRNA expression levels corresponding to each gene in at least one term, wherein the at least one term comprises term 1, term 2, term 3, term 4, term 5, term 6, or term 7; wherein term 1 comprises gene 1, gene 2, and gene 3, wherein gene 1 is AF161365, wherein gene 2 is HNRPF or ACBD5, and wherein gene 3 is TFCP2 or DDX18; wherein term 2 comprises gene 4, gene 5, and gene 6, wherein gene 4 is AF289562 or CD248, wherein gene 5 is HNRPF or ACBD5, and wherein gene 6 is TFCP2 or DDX18; wherein term 3 comprises gene 7, gene 8, gene 9, and gene 10 wherein gene 7 is CD79B or CD19, wherein gene 8 is SPIB or BLK, wherein gene 9 is CD3D or LCK, and wherein gene 10 is TMC8 or CCT2; wherein term 4 comprises gene 11, gene 12, gene 13, and gene 14, wherein gene 11 is S100A12 or MMP9, wherein gene 12 is CLEC4E or ALOX5AP, wherein gene 13 is S100A8 or NAMPT, and wherein gene 14 is RPL28 or SSRP1; wherein term 5 comprises gene 15, gene 16, gene 17, gene 18, and gene 19, wherein gene 15 is S100A12 or MMP9, wherein gene 16 is CLEC4E or ALOX5AP, wherein gene 17 is S100A8 or NAMPT, wherein gene 18 is AQP9 or GLT1D1, and wherein gene 19 is NCF4 or NCF2; wherein term 6 comprises gene 20, gene 21, gene 22, gene 23, gene 24, gene 25, and gene 26, wherein gene 20 is CASP5 or H3F3B, wherein gene 21 is IL18RAP or TXN, wherein gene 22 is TNFAIP6 or PLAUR, wherein gene 23 is IL8RB or BCL2A1, wherein gene 24 is TNFRSF10C or PTAFR, wherein gene 25 is KCNE3 or LAMP2, and wherein gene 26 is TLR4 or TYROBP; and wherein term 7 comprises gene 27, gene 28, gene 29, and gene 30, wherein gene 27 is SLAMF7 or CX3CR1, wherein gene 28 is KLRC4 or CD8A, wherein gene 29 is CD3D or LCK, and wherein gene 30 is TMC8 or CCT2; obtaining data representing age of the subject and data representing gender of the subject; and generating, by a computer processor, a score indicative of coronary artery disease (CAD) risk by mathematically combining the data representing the mRNA expression levels, the data representing the age of the subject, and the data representing the gender of the subject, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel indicates a decreased likelihood that the subject has CAD.
 2. The method of claim 1, wherein the dataset comprises data representing mRNA expression levels corresponding to each gene in term 1, term 2, term 3, term 4, term 5, term 6, and term
 7. 3. The method of claim 1, further comprising classifying the sample according to the score.
 4. The method of claim 1, further comprising rating CAD risk using the score.
 5. The method of claim 1, wherein the sample comprises RNA extracted from peripheral blood cells.
 6. The method of claim 1, wherein the subject has stable chest pain, the subject has typical angina or atypical angina or an anginal equivalent, the subject has no previous diagnosis of MI, the subject has not had a revascularization procedure, the subject does not have diabetes, the subject does not have a systemic autoimmune or infectious condition, and/or the subject is not currently taking a steroid, an immunosuppressive agent, or a chemotherapeutic agent.
 7. The method of claim 1, wherein CAD is obstructive CAD.
 8. The method of claim 1, wherein the method performance is characterized by an area under the curve (AUC) ranging from 0.68 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99.
 9. A method for determining coronary artery disease risk in a subject, comprising: performing at least one nucleotide-based assay on a sample from the subject to generate a dataset comprising data representing mRNA expression levels corresponding to at least two genes comprising AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A; obtaining data representing age of the subject and data representing gender of the subject; and generating, by a computer processor, a score indicative of CAD risk by mathematically combining the data representing the mRNA expression levels, the data representing the age of the subject, and the data representing the gender of the subject, wherein a higher score relative to a control subject having less than 50% stenosis in all major vessels indicates an increased likelihood that the subject has CAD or a lower score relative to a control subject having greater than or equal to 50% stenosis in at least one major coronary vessel indicates a decreased likelihood that the subject has CAD.
 10. The method of claim 9, further comprising classifying the sample according to the score.
 11. The method of claim 9, further comprising rating CAD risk using the score.
 12. The method of claim 9, wherein the method performance is characterized by an area under the curve (AUC) ranging from 0.68 to 0.70, 0.70 to 0.79, 0.80 to 0.89, or 0.90 to 0.99.
 13. The method of claim 9, wherein the subject has stable chest pain, the subject has typical angina or atypical angina or an anginal equivalent, the subject has no previous diagnosis of myocardial infarction (MI), the subject has not had a revascularization procedure, the subject does not have diabetes, the subject does not have a systemic autoimmune or infectious condition, and/or the subject is not currently taking a steroid, an immunosuppressive agent, or a chemotherapeutic agent.
 14. The method of claim 9, wherein CAD is obstructive CAD.
 15. A method for generating a dataset comprising data representing mRNA expression levels for a subject that has CAD or is suspected of having CAD, comprising: obtaining a sample from the subject, wherein the subject has CAD or is suspected of having CAD; performing at least one nucleotide-based assay on the sample to generate a dataset comprising data representing mRNA expression levels corresponding to each gene in at least one term, wherein the at least one term comprises term 1, term 2, term 3, term 4, term 5, term 6, or term 7; wherein term 1 comprises gene 1, gene 2, and gene 3, wherein gene 1 is AF161365, wherein gene 2 is HNRPF or ACBD5, and wherein gene 3 is TFCP2 or DDX18; wherein term 2 comprises gene 4, gene 5, and gene 6, wherein gene 4 is AF289562 or CD248, wherein gene 5 is HNRPF or ACBD5, and wherein gene 6 is TFCP2 or DDX18; wherein term 3 comprises gene 7, gene 8, gene 9, and gene 10 wherein gene 7 is CD79B or CD19, wherein gene 8 is SPIB or BLK, wherein gene 9 is CD3D or LCK, and wherein gene 10 is TMC8 or CCT2; wherein term 4 comprises gene 11, gene 12, gene 13, and gene 14, wherein gene 11 is S100A12 or MMP9, wherein gene 12 is CLEC4E or ALOX5AP, wherein gene 13 is S100A8 or NAMPT, and wherein gene 14 is RPL28 or SSRP1; wherein term 5 comprises gene 15, gene 16, gene 17, gene 18, and gene 19, wherein gene 15 is S100A12 or MMP9, wherein gene 16 is CLEC4E or ALOX5AP, wherein gene 17 is S100A8 or NAMPT, wherein gene 18 is AQP9 or GLT1D1, and wherein gene 19 is NCF4 or NCF2; wherein term 6 comprises gene 20, gene 21, gene 22, gene 23, gene 24, gene 25, and gene 26, wherein gene 20 is CASP5 or H3F3B, wherein gene 21 is IL18RAP or TXN, wherein gene 22 is TNFAIP6 or PLAUR, wherein gene 23 is IL8RB or BCL2A1, wherein gene 24 is TNFRSF10C or PTAFR, wherein gene 25 is KCNE3 or LAMP2, and wherein gene 26 is TLR4 or TYROBP; and wherein term 7 comprises gene 27, gene 28, gene 29, and gene 30, wherein gene 27 is SLAMF7 or CX3CR1, wherein gene 28 is KLRC4 or CD8A, wherein gene 29 is CD3D or LCK, and wherein gene 30 is TMC8 or CCT2.
 16. The method of claim 15, wherein the dataset comprises data representing mRNA expression levels corresponding to each gene in term 1, term 2, term 3, term 4, term 5, term 6, and term
 7. 17. The method of claim 15, wherein the sample comprises RNA extracted from peripheral blood cells.
 18. The method of claim 15, wherein the subject has stable chest pain, the subject has typical angina or atypical angina or an anginal equivalent, the subject has no previous diagnosis of MI, the subject has not had a revascularization procedure, the subject does not have diabetes, the subject does not have a systemic autoimmune or infectious condition, and/or the subject is not currently taking a steroid, an immunosuppressive agent, or a chemotherapeutic agent.
 19. The method of claim 15, wherein CAD is obstructive CAD.
 20. The method of claim 15, wherein the at least one nucleotide-based assay comprises a DNA assay, a microarray, a hybridization-based assay, a polymerase chain reaction (PCR), RT-PCR, a Southern blot, or a Northern blot. 